Journal articles: 'Automatic nerve segmentation'

1

Chrastek, Radim, Matthias Wolf, Klaus Donath, Georg Michelson, and Heinrich Niemann. "Automatic optic disc segmentation for analysis of the optic nerve head." International Congress Series 1230 (June 2001): 1174–75. http://dx.doi.org/10.1016/s0531-5131(01)00225-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Reda, Fitsum A., Jack H. Noble, Alejandro Rivas, Theodore R. McRackan, Robert F. Labadie, and Benoit M. Dawant. "Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans." Medical Physics 38, no. 10 (2011): 5590–600. http://dx.doi.org/10.1118/1.3634048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Wang, Ying, Xiaosheng Yu, Jianning Chi, and Chengdong Wu. "Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method." Mathematical Problems in Engineering 2019 (October 24, 2019): 1–10. http://dx.doi.org/10.1155/2019/4836296.

Full text

Abstract:

Glaucoma is a group of eye conditions, which can seriously damage optic nerves because of an elevated intraocular pressure. Nowadays, glaucoma has become one of the principal causes of blindness that results in irreversible visual loss. Early screening and treatment of glaucoma can prevent further progression of optic nerve degeneration effectively. The vertical cup-to-disc ratio (CDR) is a commonly used measurement for the detection of glaucoma, and therefore accurate segmentation of optic disc (OD) and optic cup (OC) regions in retinal fundus images is of great significance. In this paper, we present a prior shape constraint-based two-layer level set method for OD and OC segmentation in fundus images. This method uses two different layers of one level set function to represent the OD and OC boundaries. In this method, the distance regularized term is designed to guarantee that the distance between the OD and OC varies smoothly. By introducing the prior shape constraint term energy, the final segmentation results of OD and OC are always in the shape of approximate ellipses. In addition, the proposed method has the property of dealing with the intensity inhomogeneity of fundus images through the local fitting energy embedded. Experiments on images from the Baidu Research database demonstrate that the proposed method has superior performance in terms of accuracy and effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

4

Raman, Murugan, Reeba Korah, and Kavitha Tamilselvan. "An Automatic Localization of Optic Disc in Low Resolution Retinal Images by Modified Directional Matched Filter." International Arab Journal of Information Technology 16, no. 1 (2018): 1–7. http://dx.doi.org/10.34028/iajit/16/1/1.

Full text

Abstract:

An automatic optic disc localization in retinal images used to screen eye related diseases like diabetic retinopathy. Many techniques are available to detect Optic Disc (OD) in high-resolution retinal images. Unfortunately, there are no efficient methods available to detect OD in low-resolution retinal images. The objective of this research paper is to develop an automated method for localization of Optic Disc in low resolution retinal images. This paper proposes a modified directional matched filter parameters of the retinal blood vessels to localize the center of optic disc. The proposed method was implemented in MATLAB and evaluated both normal and abnormal low resolution retinal images using the subset of Optic Nerve Head Segmentation Dataset (ONHSD) and the success percentage was found to be an average of 96.96% with 23seconds

APA, Harvard, Vancouver, ISO, and other styles

5

Smistad, Erik, Daniel Høyer Iversen, Linda Leidig, Janne Beate Lervik Bakeng, Kaj Fredrik Johansen, and Frank Lindseth. "Automatic Segmentation and Probe Guidance for Real-Time Assistance of Ultrasound-Guided Femoral Nerve Blocks." Ultrasound in Medicine & Biology 43, no. 1 (2017): 218–26. http://dx.doi.org/10.1016/j.ultrasmedbio.2016.08.036.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Hidalgo-Aguirre, Maribel, Julian Gitelman, Mark Richard Lesk, and Santiago Costantino. "Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence tomography." Journal of Biomedical Optics 20, no. 11 (2015): 116008. http://dx.doi.org/10.1117/1.jbo.20.11.116008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Jiang, Chaoqun, Jianhuang Wu, Weizheng Zhong, et al. "Automatic Facial Paralysis Assessment via Computational Image Analysis." Journal of Healthcare Engineering 2020 (February 8, 2020): 1–10. http://dx.doi.org/10.1155/2020/2398542.

Full text

Abstract:

Facial paralysis (FP) is a loss of facial movement due to nerve damage. Most existing diagnosis systems of FP are subjective, e.g., the House–Brackmann (HB) grading system, which highly depends on the skilled clinicians and lacks an automatic quantitative assessment. In this paper, we propose an efficient yet objective facial paralysis assessment approach via automatic computational image analysis. First, the facial blood flow of FP patients is measured by the technique of laser speckle contrast imaging to generate both RGB color images and blood flow images. Second, with an improved segmentation approach, the patient’s face is divided into concerned regions to extract facial blood flow distribution characteristics. Finally, three HB score classifiers are employed to quantify the severity of FP patients. The proposed method has been validated on 80 FP patients, and quantitative results demonstrate that our method, achieving an accuracy of 97.14%, outperforms the state-of-the-art systems. Experimental evaluations also show that the proposed approach could yield objective and quantitative FP diagnosis results, which agree with those obtained by an experienced clinician.

APA, Harvard, Vancouver, ISO, and other styles

8

Noble, Jack H., Frank M. Warren, Robert F. Labadie, and Benoit M. Dawant. "Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values." Medical Physics 35, no. 12 (2008): 5375–84. http://dx.doi.org/10.1118/1.3005479.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Zhang, Xiaomang, Keiji Taniguchi, and Rokuro Matsubara. "An Automatic Image Segmentation Method for Nerve Cell Bodies in the Locus Ceruleus of the Human Brain." IEEJ Transactions on Electronics, Information and Systems 114, no. 5 (1994): 569–78. http://dx.doi.org/10.1541/ieejeiss1987.114.5_569.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Smistad, Erik, and Frank Lindseth. "Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve." IEEE Transactions on Medical Imaging 35, no. 3 (2016): 752–61. http://dx.doi.org/10.1109/tmi.2015.2494160.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Erwin, Erwin, Saparudin Saparudin, Arum Cantika Putri, Hidayat Hidayat, and Fifi Hariyani. "Feature Extraction for Retina Image Based on Difference Approaches." Computer Engineering and Applications Journal 7, no. 3 (2018): 205–21. http://dx.doi.org/10.18495/comengapp.v7i3.275.

Full text

Abstract:

Automatic disease diagnosis using biometric images is a difficult job due to image distortion, such as the presence of artifacts, less or excessive light, narrow vessel visibility and differences in inter-camera variability that affect the performance of an approaches. Almost all extraction methods in the blood vessels in the retina produce the main part of the vessel with no patalogical environment. However, an important problem for this method is that extraction errors occur if they are too focused on the thin vessels, the wide vessels will be more detectable and also artificial vessels that may appear a lot. In addition, when focusing on a wide vessel, the extraction of thin vessels tends to disappear and is low. Based on our research, different treatments are needed to extract thin vessels and wide vessels both visually and in contrast. This study aims to adopt feature extraction strategies with different techniques. The method proposed in segmentation and extraction with three different approaches, namely the pattern of shape, color, and texture. Testing segmentation and feature extraction using STARE datasets with five classes of diseases namely Choroidal Neovascularization, Branch Retinal Vein Occlusion, Histoplasmosis, Myelinated Nerve Fibers, and Coats. Image enhancement on Myelinated Nerve disease Fiber is the best result from the image of other diseases with the highest value of PSNR of 35.4933 dB and the lowest MSE of 0.0003 means that the technique is able to repair objects well. The main significance of this work is to increase the quality of segmentation results by applying the Otsu method. Testing of segmentation results shows improvements with the proposed method compared to other methods. Furthermore, the application of different feature extraction methods will get information on disease classification features based on patterns of suitable shapes, colors, and textures.

APA, Harvard, Vancouver, ISO, and other styles

12

Al-Fahdawi, Shumoos, Rami Qahwaji, Alaa S. Al-Waisy, et al. "A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images." Computer Methods and Programs in Biomedicine 135 (October 2016): 151–66. http://dx.doi.org/10.1016/j.cmpb.2016.07.032.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Duan, Yifei, Jennifer Sweet, Charles Munyon, and Jonathan Miller. "Degree of distal trigeminal nerve atrophy predicts outcome after microvascular decompression for Type 1a trigeminal neuralgia." Journal of Neurosurgery 123, no. 6 (2015): 1512–18. http://dx.doi.org/10.3171/2014.12.jns142086.

Full text

Abstract:

OBJECT Trigeminal neuralgia is often associated with nerve atrophy, in addition to vascular compression. The authors evaluated whether cross-sectional areas of different portions of the trigeminal nerve on preoperative imaging could be used to predict outcome after microvascular decompression (MVD). METHODS A total of 26 consecutive patients with unilateral Type 1a trigeminal neuralgia underwent high-resolution fast-field echo MRI of the cerebellopontine angle followed by MVD. Preoperative images were reconstructed and reviewed by 2 examiners blinded to the side of symptoms and clinical outcome. For each nerve, a computerized automatic segmentation algorithm was used to calculate the coronal cross-sectional area at the proximal nerve near the root entry zone and the distal nerve at the exit from the porus trigeminus. Findings were correlated with outcome at 12 months. RESULTS After MVD, 17 patients were pain free and not taking medications compared with 9 with residual pain. Across all cases, the coronal cross-sectional area of the symptomatic trigeminal nerve was significantly smaller than the asymptomatic side in the proximal part of the nerve, which was correlated with degree of compression at surgery. Atrophy of the distal trigeminal nerve was more pronounced in patients who had residual pain than in those with excellent outcome. Among the 7 patients who had greater than 20% loss of nerve volume in the distal nerve, only 2 were pain free and not taking medications at long-term follow-up. CONCLUSIONS Trigeminal neuralgia is associated with atrophy of the root entry zone of the affected nerve compared with the asymptomatic side, but volume loss in different segments of the nerve has very different prognostic implications. Proximal atrophy is associated with vascular compression and correlates with improved outcome following MVD. However, distal atrophy is associated with a significantly worse outcome after MVD.

APA, Harvard, Vancouver, ISO, and other styles

14

Diaz-Pinto, Andres, Sandra Morales, Valery Naranjo, and Amparo Navea. "Computer-Aided Glaucoma Diagnosis Using Stochastic Watershed Transformation on Single Fundus Images." Journal of Medical Imaging and Health Informatics 9, no. 6 (2019): 1057–65. http://dx.doi.org/10.1166/jmihi.2019.2721.

Full text

Abstract:

Glaucoma is a chronic eye disease and one of the major causes of permanent blindness. Since it does not show initial symptoms, early diagnosis is important to limit its progression. This paper presents an automatic optic nerve characterization algorithm for glaucoma diagnosis based only on retinal fundus images. For optic cup segmentation, we used a new method based on the stochastic watershed transformation applied on the YIQ colour space to extract clinical indicators such as the Cup/Disc ratio, the area Cup/Disc ratio and the ISNT rule. Afterwards, an assessment between normal and glaucomatous fundus images is performed. The proposed algorithm was evaluated on 6 different (private and public) databases containing 723 images (377 normal and 346 glaucomatous images) which achieved a specificity and sensitivity of 0.674 and 0.675, respectively. Moreover, an F-score of 0.770 was obtained when evaluating this method on the publicly available database Drishti-GS1. A comparison of the proposed work with other state-of-the-art methods demonstrates the robustness of the proposed algorithm; because it was tested using images from different databases with high variability, which is a common issue in this area. Additional comparisons with existing works for cup segmentation, that use the publicly available database Drishti-GS1, are also presented in this paper.

APA, Harvard, Vancouver, ISO, and other styles

15

Su, Haoliang, Fang Wang, Leying Zhang, and Guiyang Li. "Fuzzy Clustering Algorithm-Segmented MRI Images in Analysis of Effects of Mental Imagery on Neurorehabilitation of Stroke Patients." Scientific Programming 2021 (July 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/9945153.

Full text

Abstract:

The study focused on the automatic segmentation of Magnetic Resonance Imaging (MRI) images of stroke patients and the therapeutic effects of Mental Imagery on motor and neurological functions after stroke. First, the traditional fuzzy c-means (FCM) algorithm was optimized, and the optimized one was defined as filter-based FCM (FBFCM). 62 stroke patients were selected as the research subjects and randomly divided into the experimental group and the control group. The control group accepted the conventional rehabilitation training, and the experimental group accepted Mental Imagery on the basis of the control group. They all had the MRI examination, and their brain MRI images were segmented by the FBFCM algorithm. The MRI images before and after treatment were analyzed to evaluate the therapeutic effects of Mental Imagery on patients with motor and nerve dysfunction after stroke. The results showed that the segmentation coefficient of the FBFCM algorithm was 0.9315 and the segmentation entropy was 0.1098, which were significantly different from those of the traditional fuzzy c-means (FCM) algorithm. ( P < 0.05 ), suggesting that the FBFCM algorithm had good segmentation effects on brain MRI images of stroke patients. After Mental Imagery, it was found that the patient’s Function Independent Measure (FIM) score was 99.04 ± 8.19, the Modified Barthel Index (MBI) score was 51.29 ± 4.35, the Fugl-Meyer (FMA) score was 61.01 ± 4.16, the neurological deficit degree in stroke (NFDS) score was 11.48 ± 2.01, the NIH Stroke Scale (NIHSS) score was 10.36 ± 1.69, and the clinical effective rate was 87.1%, all significantly different from those of the conventional rehabilitation training group ( P < 0.05 ). Additionally, the brain area activated by Mental Imagery was more extensive. In conclusion, the FBFCM algorithm demonstrates superb capabilities in segmenting MRI images of stroke patients and is worth promotion in clinic. Mental Imagery can promote the neurological rehabilitation of patients by activating relevant brain areas of patients.

APA, Harvard, Vancouver, ISO, and other styles

16

Vujosevic, Stela, and Edoardo Midena. "Retinal Layers Changes in Human Preclinical and Early Clinical Diabetic Retinopathy Support Early Retinal Neuronal and Müller Cells Alterations." Journal of Diabetes Research 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/905058.

Full text

Abstract:

Purpose. To evaluate the changes in thickness of individual inner and outer macular and peripapillary retinal layers in diabetes.Methods. 124 subjects (124 eyes) were enrolled: 74 diabetics and 50 controls. Macular edema, proliferative diabetic retinopathy (DR), any intraocular treatment and refractive error>6diopters were the main exclusion criteria. Full ophthalmic examination, stereoscopic fundus photography, and spectral domain-OCT were performed. After automatic retinal segmentation (layering) in 5 layers, the thickness of each layer was calculated, and values compared among groups.Results. Thirty patients had no DR, 44 patients had non proliferative DR. A significant increase of inner plexiform and nuclear layers was found in DR eyes versus controls (P<0.001). A significant decrease (P<0.01) of retinal nerve fiber layer (RNFL) and at specific sites of retinal ganglion cell layer (P=0.02) was documented in the macula. In the peripapillary area there were no differences between diabetics and controls.Conclusions. Decreased RNFL thickness and increased INL/OPL thickness in diabetics without DR or with initial DR suggest early alterations in the inner retina. On the contrary, the outer retina seems not to be affected at early stages of DM. Automatic intraretinal layering by SD-OCT may be a useful tool to diagnose and monitor early intraretinal changes in DR.

APA, Harvard, Vancouver, ISO, and other styles

17

Balkys, Gediminas, and Gintautas Dzemyda. "SEGMENTING THE EYE FUNDUS IMAGES FOR IDENTIFICATION OF BLOOD VESSELS." Mathematical Modelling and Analysis 17, no. 1 (2012): 21–30. http://dx.doi.org/10.3846/13926292.2012.644046.

Full text

Abstract:

Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture.

APA, Harvard, Vancouver, ISO, and other styles

18

Kasaragod, Deepa, Shuichi Makita, Young-Joo Hong, and Yoshiaki Yasuno. "Machine-learning based segmentation of the optic nerve head using multi-contrast Jones matrix optical coherence tomography with semi-automatic training dataset generation." Biomedical Optics Express 9, no. 7 (2018): 3220. http://dx.doi.org/10.1364/boe.9.003220.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Zolnikova, I. V., S. V. Milash, A. B. Chernyak, et al. "Retinal Postphotoreceptor Layers and Macular Electroretinogram in Retinitis Pigmentosa." Ophthalmology in Russia 17, no. 1 (2020): 81–87. http://dx.doi.org/10.18008/1816-5095-2020-1-81-87.

Full text

Abstract:

Purpose: to assess the inner retinal layer’s changes of the central retina in comparison with bioelectrical activity of macula assessed by MERG and with thickness of photoreceptor retinal layers in patients with retinitis pigmentosa.Patients and methods: 10 patients (20 phakic eyes) with RP were examined. Mean age was 27.0 ± 18.5 years. Mean best corrected visual acuity was 0.38 ± 0.22. The fundus photography electrophysiological studies were performed besides standard ophthalmological examination. Maximal electroretinogram (ERG) or cone-rod response, ERG to 30 Hz flicker and macular ERG were registered with electroretinograph MBN (Russia). Images were acquired using the protocol of scanning Macula radial (12 lines 9 mm long, each radial image is the average out of 50 scans) with settings ultrafine with automatic segmentation of the retina and calculation thickness. Retinal segmentation with the following calculation of different retinal layers was performed on SD-OCT RS-3000 Advance (Nidek, Japan) with obtainment of the map with 6 mm in diameter in accordance with the Early Treatment Diabetic Retinopathy Study (ETDRS).Results. We revealed increased thickness of retinal nerve fiber layer (RNFL), decrease of thickness of ganglion cell layer (GCL) (p < 0,05) and normal (p > 0,05) inner nuclear layer (INL) thickness. These structural changes are associated with the decrease of bioelectric macula’s activity measured by MERG (decrease of α- and β-wave amplitude) and decrease the thickness of photoreceptor layers in all zones according to EDTRS.Conclusion. Increase of thickness RNFL, decrease of GCL’s thickness and normal INL in patients with RP is associated with decrease of thickness of photoreceptor layers in EDTRS zones and decrease of amplitude of MERG.

APA, Harvard, Vancouver, ISO, and other styles

20

Xia, Zheren, Hao Chen, and Suilian Zheng. "Alterations of Retinal Pigment Epithelium–Photoreceptor Complex in Patients with Type 2 Diabetes Mellitus without Diabetic Retinopathy: A Cross-Sectional Study." Journal of Diabetes Research 2020 (March 6, 2020): 1–6. http://dx.doi.org/10.1155/2020/9232157.

Full text

Abstract:

Aim. A cross-sectional study was performed to examine the alterations of the retinal pigment epithelium– (RPE–) photoreceptor complex layer in type 2 diabetes mellitus (DM) without diabetic retinopathy (DR), using spectral-domain optical coherence tomography (SD-OCT). Methods. Patients with type 2 DM without DR and healthy controls without DM were recruited. All participants underwent examinations including SD-OCT. The thickness measurements of the retinal neural layers were calculated after automatic segmentation. An independent-sample t-test was used to compare the means of the thickness of retinal neural layers in patients with DM and healthy controls. Results. Sixty-seven eyes from 67 patients with DM and 30 eyes from 30 healthy controls were included in this study. No significant differences were found in age (P = 0.601), gender (P = 0.560), axial length (P = 0.414), best-corrected visual acuity (P = 0.963), or intraocular pressure (P = 0.112) between the two groups. There were significant increases in the hemoglobin A1c value (P < 0.001) and mean thicknesses of the RPE–photoreceptor complex layer in the foveal area (P = 0.027) and paracentral area (P = 0.001) in the DM group compared to the control group, whereas the thickness of the retinal nerve fiber and ganglion cell layers in the foveal and paracentral areas between the two groups showed no significant differences. Conclusion. Lesions in the RPE–photoreceptor complex are present without vascular abnormalities, which may precede the alterations of ganglion cells in patients with type 2 DM.

APA, Harvard, Vancouver, ISO, and other styles

21

Yoo, Yung Ju, Jeong-Min Hwang, and Hee Kyung Yang. "Inner macular layer thickness by spectral domain optical coherence tomography in children and adults: a hospital-based study." British Journal of Ophthalmology 103, no. 11 (2019): 1576–83. http://dx.doi.org/10.1136/bjophthalmol-2018-312349.

Full text

Abstract:

PurposeTo establish the normative ranges of macular ganglion cell layer (mGCL) and macular inner plexiform layer (mIPL) thickness using Spectralis spectral domain optical coherence tomography (SD-OCT) (Heidelberg Engineering, Inc., Heidelberg, Germany) in both Korean children and adults, and to determine factors associated with mGCL and mIPL thickness.MethodsWe conducted a retrospective, observational study of 573 healthy subjects (5–70 years old) who underwent comprehensive ophthalmic examinations in a single institution. Each inner retinal layer thickness was measured using SD-OCT and automatic segmentation software. Cross-sectional analysis was used to evaluate the effect of gender, age and ocular parameters on mGCL and mIPL thickness. Normative ranges of mGCL and mIPL thickness according to age, gender and factors associated with mGCL and mIPL thickness were measured.ResultsThe mean mGCL and mIPL thickness were 40.6±2.8 and 33.8±2.0 µm, respectively. Determinants of inner sector mGCL thickness were circumpapillary retinal nerve fibre layer (cpRNFL) thickness (β=1.172, p<0.001), age (β=−0.019, p=0.021) and male gender (β=1.452, p<0.001). Determinants of inner sector mIPL thickness were cpRNFL (β=0.952, p<0.001) and male gender (β=1.163, p<0.001). The inner sector mGCL and mIPL thickness increased significantly with age in children (β=0.174, p=0.009 and β=0.115, p=0.013), and then decreased in adults (β=−0.070, p<0.001 and β=−0.024, p=0.032). In the case of outer sectors, mGCL and mIPL thickness were not significantly related to age and gender.ConclusionsThis study ensured a normative range of the mGCL and mIPL thickness using Spectralis OCT. Gender, age and cpRNFL thickness significantly correlated with mGCL and mIPL thickness. This information should be considered in the interpretation of SD-OCT data.

APA, Harvard, Vancouver, ISO, and other styles

22

Ferreira, Ana, António Miguel Morgado, and José Silvestre Silva. "A method for corneal nerves automatic segmentation and morphometric analysis." Computer Methods and Programs in Biomedicine 107, no. 1 (2012): 53–60. http://dx.doi.org/10.1016/j.cmpb.2011.09.014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Faizal, Z., and Syed Usama. "Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering." International Journal of Applied Information Systems 9, no. 6 (2015): 7–10. http://dx.doi.org/10.5120/ijais2015451423.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Danyluk, Hayden, Esther Kyungsu Lee, Scott Wong, et al. "Hippocampal and trigeminal nerve volume predict outcome of surgical treatment for trigeminal neuralgia." Cephalalgia 40, no. 6 (2019): 586–96. http://dx.doi.org/10.1177/0333102419877659.

Full text

Abstract:

Background Many medically-refractory trigeminal neuralgia patients are non-responders to surgical treatment. Few studies have explored how trigeminal nerve characteristics relate to surgical outcome, and none have investigated the relationship between subcortical brain structure and treatment outcomes. Methods We retrospectively studied trigeminal neuralgia patients undergoing surgical treatment with microvascular decompression. Preoperative magnetic resonance imaging was used for manual tracing of trigeminal nerves and automated segmentation of hippocampus, amygdala, and thalamus. Nerve and subcortical structure volumes were compared between responders and non-responders and assessed for ability to predict postoperative pain outcome. Results In all, 359 trigeminal neuralgia patients treated surgically from 2005–2018 were identified. A total of 34 patients met the inclusion criteria (32 with classic and two with idiopathic trigeminal neuralgia). Across all patients, thalamus volume was reduced ipsilateral compared to contralateral to the side of pain. Between responders and non-responders, non-responders exhibited larger contralateral trigeminal nerve volume, and larger ipsilateral and contralateral hippocampus volume. Through receiver-operator characteristic curve analyses, contralateral hippocampus volume correctly classified treatment outcome in 82% of cases (91% sensitive, 78% specific, p = 0.008), and contralateral nerve volume correctly classified 81% of cases (91% sensitive, 75% specific, p < 0.001). Binomial logistic regression analysis showed that contralateral hippocampus and contralateral nerve volumes together classified outcome with 84% accuracy (Nagelkerke R2 = 65.1). Conclusion Preoperative hippocampal and trigeminal nerve volume, measured on standard clinical magnetic resonance images, may predict early non-response to surgical treatment for trigeminal neuralgia. Treatment resistance in medically refractory trigeminal neuralgia may depend on the structural features of both the trigeminal nerve and structures involved in limbic components of chronic pain.

APA, Harvard, Vancouver, ISO, and other styles

25

Chrástek, R., M. Wolf, K. Donath, et al. "Automated segmentation of the optic nerve head for diagnosis of glaucoma." Medical Image Analysis 9, no. 4 (2005): 297–314. http://dx.doi.org/10.1016/j.media.2004.12.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Almobarak, Faisal A., Neil O'Leary, Alexandre S. C. Reis, et al. "Automated Segmentation of Optic Nerve Head Structures With Optical Coherence Tomography." Investigative Opthalmology & Visual Science 55, no. 2 (2014): 1161. http://dx.doi.org/10.1167/iovs.13-13310.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

R, Chandana. "Retinal Vessel Analysis for Detection of Glaucoma." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3661–67. http://dx.doi.org/10.22214/ijraset.2021.37199.

Full text

Abstract:

Glaucoma, a disease of the optic nerve is caused by the increase in the intraocular pressure of the eye and results in damage to the optic nerve and vision loss. The main characteristic of glaucoma is an elevated intraocular pressure (IOP) and also the blood vessels get narrower. Vessel segmentation is one of the main steps in retinal automated analysis tools. Retinal vessel segmentation and delineation of morphological attributes of retinal blood vessels are utilized for diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. Since, the numbers of blood vessels are more in the glaucomatous eye , glaucoma is detected by means of ISNT ratio. The image processing operations are performed on glaucomatous and normal eyes. We have chosen ten images of each from the database and ISNT ratio is calculated to get the area of blood vessels in each of the four quadrants of the eye and hence glaucoma is detected.

APA, Harvard, Vancouver, ISO, and other styles

28

Chen, Yen Sheng, Shao Hsien Chen, and Jeih Jang Liou. "Comparison of Multispectral Image Processing Techniques to Brain MR Image Classification." Applied Mechanics and Materials 182-183 (June 2012): 1998–2002. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1998.

Full text

Abstract:

Brain Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities. Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers. This nerve damage is visible in Magnetic Resonance (MR) scans of the brain. Manual segmentation is extremely time-consuming and tedious. Therefore, fully automated MS detection methods are being developed which can classify large amounts of MR data, and do not suffer from inter observer variability. In this paper we use standard fuzzy c-means algorithm (FCM) for multi-spectral images to segment patient MRI data. Geodesic Active Contours of Caselles level set is another method we implement to do the brain image segmentation jobs. And then we implement anther modified Fuzzy C-Means algorithm, where we call Bias-Corrected FCM as BCFCM, for bias field estimation for the same thing. Experimental results show the success of all these intelligent techniques for brain medical image segmentation.

APA, Harvard, Vancouver, ISO, and other styles

29

Saeed, A. N. "A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks." Engineering, Technology & Applied Science Research 10, no. 4 (2020): 5986–91. http://dx.doi.org/10.48084/etasr.3666.

Full text

Abstract:

Artificial Intelligence (AI) based Machine Learning (ML) is gaining more attention from researchers. In ophthalmology, ML has been applied to fundus photographs, achieving robust classification performance in the detection of diseases such as diabetic retinopathy, retinopathy of prematurity, etc. The detection and extraction of blood vessels in the retina is an essential part of various diagnosing problems associated with eyes, such as diabetic retinopathy. This paper proposes a novel machine learning approach to segment the retinal blood vessels from eye fundus images using a combination of color features, texture features, and Back Propagation Neural Networks (BPNN). The proposed method comprises of two steps, namely the color texture feature extraction and training the BPNN to get the segmented retinal nerves. Magenta color and correlation-texture features are given as input to the BPNN. The system was trained and tested in retinal fundus images taken from two distinct databases. The average sensitivity, specificity, and accuracy obtained for the segmentation of retinal blood vessels were 0.470%, 0.914%, and 0.903% respectively. Results obtained reveal that the proposed methodology is excellent in automated segmentation retinal nerves. The proposed segmentation methodology was able to obtain comparable accuracy with other methods.

APA, Harvard, Vancouver, ISO, and other styles

30

FaizalKhan, Z., and Syed Usama Quadri. "Automated Segmentation of Optical Nerves by Neural Network based Region Growing." Communications on Applied Electronics 1, no. 5 (2015): 9–13. http://dx.doi.org/10.5120/cae-1543.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Festen, Raymond T., Verena J. M. M. Schrier, and Peter C. Amadio. "Automated Segmentation of the Median Nerve in the Carpal Tunnel using U-Net." Ultrasound in Medicine & Biology 47, no. 7 (2021): 1964–69. http://dx.doi.org/10.1016/j.ultrasmedbio.2021.03.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Liang, Liming, Xiaoqi Sheng, Bowen Liu, and Zhimin Lan. "A Level Set Method with Region-Scalable Fitting Energy for Retinal Layer Segmentation in Spectral-Domain Optical Coherence Tomography Images." Journal of Medical Imaging and Health Informatics 10, no. 2 (2020): 326–35. http://dx.doi.org/10.1166/jmihi.2020.2905.

Full text

Abstract:

Retinal layer segmentation of spectral-domain optical coherence tomography images plays an important role during diagnosis and analysis of ophthalmic diseases. In this paper, a novel variational level set framework with region-scalable fitting energy is proposed for automated retinal layer segmentation in SD-OCT. To the best of our knowledge, it is the first time that level set based method succeeds in ten retinal layers segmentation. The proposed framework consists of three steps. First, an anisotropic nonlinear diffusion filter is applied for speckle noise reduction and ROI contrast enhancement. Second, Canny edge detectors are used to extract initial layers: nerve fiber layer, connecting cilia and retinal pigment epithelium. Finally, the rest retinal layers are segmented by means of level set model combined with prior knowledge of retinal thickness and morphology, for which the energy function consists of region-scalable fitting energy data term, area constraint term, regularization term and length penalty term. The proposed method was tested on 50 retinal SD-OCT B-scans from 50 normal subjects. The overall unsigned border position error is 5.92 ± 4.72 μm. The result showed that data terms with border weight terms can keep layer segmentation results in strong border while retaining its fitting capability in weak border. The proposed method achieves better segmentation result than other active contour models.

APA, Harvard, Vancouver, ISO, and other styles

33

Chu, Carlton, Jeffrey De Fauw, Nenad Tomasev, et al. "Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans." F1000Research 5 (August 30, 2016): 2104. http://dx.doi.org/10.12688/f1000research.9525.1.

Full text

Abstract:

Radiotherapy is one of the main ways head and neck cancers are treated; radiation is used to kill cancerous cells and prevent their recurrence. Complex treatment planning is required to ensure that enough radiation is given to the tumour, and little to other sensitive structures (known as organs at risk) such as the eyes and nerves which might otherwise be damaged. This is especially difficult in the head and neck, where multiple at-risk structures often lie in extremely close proximity to the tumour. It can take radiotherapy experts four hours or more to pick out the important areas on planning scans (known as segmentation). This research will focus on applying machine learning algorithms to automatic segmentation of head and neck planning computed tomography (CT) and magnetic resonance imaging (MRI) scans at University College London Hospital NHS Foundation Trust patients. Through analysis of the images used in radiotherapy DeepMind Health will investigate improvements in efficiency of cancer treatment pathways.

APA, Harvard, Vancouver, ISO, and other styles

34

Duan, Xiangyun J., Joan L. Jefferys, and Harry A. Quigley. "Evaluation of Automated Segmentation Algorithms for Optic Nerve Head Structures in Optical Coherence Tomography Images." Investigative Opthalmology & Visual Science 59, no. 10 (2018): 3816. http://dx.doi.org/10.1167/iovs.18-24469.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Fan, G., H. Liu, Z. Wu, et al. "Deep Learning–Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study." American Journal of Neuroradiology 40, no. 6 (2019): 1074–81. http://dx.doi.org/10.3174/ajnr.a6070.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Yildiz, Erdost, Abdullah Taha Arslan, Ayse Yildiz Tas, et al. "Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images." Translational Vision Science & Technology 10, no. 6 (2021): 33. http://dx.doi.org/10.1167/tvst.10.6.33.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Bähr, Friederike, Burkhard Gess, Madlaine Müller, et al. "Semi-Automatic MRI Muscle Volumetry to Diagnose and Monitor Hereditary and Acquired Polyneuropathies." Brain Sciences 11, no. 2 (2021): 202. http://dx.doi.org/10.3390/brainsci11020202.

Full text

Abstract:

With emerging treatment approaches, it is crucial to correctly diagnose and monitor hereditary and acquired polyneuropathies. This study aimed to assess the validity and accuracy of magnet resonance imaging (MRI)-based muscle volumetry.Using semi-automatic segmentations of upper- and lower leg muscles based on whole-body MRI and axial T1-weighted turbo spin-echo sequences, we compared and correlated muscle volumes, and clinical and neurophysiological parameters in demyelinating Charcot-Marie-Tooth disease (CMT) (n = 13), chronic inflammatory demyelinating polyneuropathy (CIDP) (n = 27), and other neuropathy (n = 17) patients.The muscle volumes of lower legs correlated with foot dorsiflexion strength (p < 0.0001), CMT Neuropathy Score 2 (p < 0.0001), early gait disorders (p = 0.0486), and in CIDP patients with tibial nerve conduction velocities (p = 0.0092). Lower (p = 0.0218) and upper (p = 0.0342) leg muscles were significantly larger in CIDP compared to CMT patients. At one-year follow-up (n = 15), leg muscle volumes showed no significant decrease.MRI muscle volumetry is a promising method to differentiate and characterize neuropathies in clinical practice.

APA, Harvard, Vancouver, ISO, and other styles

38

Oberwahrenbrock, Timm, Ghislaine L. Traber, Sebastian Lukas, et al. "Multicenter reliability of semiautomatic retinal layer segmentation using OCT." Neurology - Neuroimmunology Neuroinflammation 5, no. 3 (2018): e449. http://dx.doi.org/10.1212/nxi.0000000000000449.

Full text

Abstract:

ObjectiveTo evaluate the inter-rater reliability of semiautomated segmentation of spectral domain optical coherence tomography (OCT) macular volume scans.MethodsMacular OCT volume scans of left eyes from 17 subjects (8 patients with MS and 9 healthy controls) were automatically segmented by Heidelberg Eye Explorer (v1.9.3.0) beta-software (Spectralis Viewing Module v6.0.0.7), followed by manual correction by 5 experienced operators from 5 different academic centers. The mean thicknesses within a 6-mm area around the fovea were computed for the retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer, outer plexiform layer (OPL), and outer nuclear layer (ONL). Intraclass correlation coefficients (ICCs) were calculated for mean layer thickness values. Spatial distribution of ICC values for the segmented volume scans was investigated using heat maps.ResultsAgreement between raters was good (ICC > 0.84) for all retinal layers, particularly inner retinal layers showed excellent agreement across raters (ICC > 0.96). Spatial distribution of ICC showed highest values in the perimacular area, whereas the ICCs were poorer for the foveola and the more peripheral macular area. The automated segmentation of the OPL and ONL required the most correction and showed the least agreement, whereas differences were less prominent for the remaining layers.ConclusionsAutomated segmentation with manual correction of macular OCT scans is highly reliable when performed by experienced raters and can thus be applied in multicenter settings. Reliability can be improved by restricting analysis to the perimacular area and compound segmentation of GCL and IPL.

APA, Harvard, Vancouver, ISO, and other styles

39

Abràmoff, Michael D., Kyungmoo Lee, Meindert Niemeijer, et al. "Automated Segmentation of the Cup and Rim from Spectral Domain OCT of the Optic Nerve Head." Investigative Opthalmology & Visual Science 50, no. 12 (2009): 5778. http://dx.doi.org/10.1167/iovs.09-3790.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Zahavi, Ori, Alberto Domínguez-Vicent, Rune Brautaset, and Abinaya Priya Venkataraman. "Evaluation of Automated Segmentation Algorithm for Macular Volumetric Measurements of Eight Individual Retinal Layer Thickness." Applied Sciences 11, no. 3 (2021): 1250. http://dx.doi.org/10.3390/app11031250.

Full text

Abstract:

Background: We evaluated the performance of an automated algorithm available on a clinical OCT (Canon-HS100) for macular volumetric measurements of eight individual retinal layers. Methods and Analysis: Two consecutive three-dimensional scans were acquired on 29 subjects with healthy retinas. Thickness measurements were obtained from eight individual retinal layers in nine macular sectors based on Early Treatment Diabetic Retinopathy Study (ETDRS) protocol. The repeatability was evaluated using the within-subject standard deviation from which the repeatability limits (Rlimit) and coefficient of variation (CoV) were calculated. Results: The repeatability metrics varied among different layers and sectors. The variation among the sectors was larger in two of the outer layers (plexiform and nuclear layer) and the retinal nerve fiber layer. For the other five layers, the repeatability limit was less than 5µm and CoV was less than 7.5% in all nine ETDRS sectors. Conclusions: The repeatability of the OCT-HS100 to measure eight individual retinal layers is good in general. Nevertheless, the repeatability is not homogeneous among different layers and sectors. This needs to be taken into account while designing clinical measurement protocols.

APA, Harvard, Vancouver, ISO, and other styles

41

Song, Hwa-Seob, Hyun-Soo Yoon, Seongpung Lee, Chang-Ki Hong, and Byung-Ju Yi. "Surgical Navigation System for Transsphenoidal Pituitary Surgery Applying U-Net-Based Automatic Segmentation and Bendable Devices." Applied Sciences 9, no. 24 (2019): 5540. http://dx.doi.org/10.3390/app9245540.

Full text

Abstract:

Conventional navigation systems used in transsphenoidal pituitary surgery have limitations that may lead to organ damage, including long image registration time, absence of alarms when approaching vital organs and lack of 3-D model information. To resolve the problems of conventional navigation systems, this study proposes a U-Net-based, automatic segmentation algorithm for optical nerves and internal carotid arteries, by training patient computed tomography angiography images. The authors have also developed a bendable endoscope and surgical tool to eliminate blind regions that occur when using straight, rigid, conventional endoscopes and surgical tools during transsphenoidal pituitary surgery. In this study, the effectiveness of a U-Net-based navigation system integrated with bendable surgical tools and a bendable endoscope has been demonstrated through phantom-based experiments. In order to measure the U-net performance, the Jaccard similarity, recall and precision were calculated. In addition, the fiducial and target registration errors of the navigation system and the accuracy of the alarm warning functions were measured in the phantom-based environment.

APA, Harvard, Vancouver, ISO, and other styles

42

Mansberger, Steven L., Shivali A. Menda, Brad A. Fortune, Stuart K. Gardiner, and Shaban Demirel. "Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma." American Journal of Ophthalmology 174 (February 2017): 1–8. http://dx.doi.org/10.1016/j.ajo.2016.10.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Wei, Shanshan, Faqiang Shi, Yuexin Wang, Yilin Chou, and Xuemin Li. "A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy." Translational Vision Science & Technology 9, no. 2 (2020): 32. http://dx.doi.org/10.1167/tvst.9.2.32.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Dzyubak, Oleksandr P., and Erik L. Ritman. "Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images." International Journal of Biomedical Imaging 2011 (2011): 1–16. http://dx.doi.org/10.1155/2011/920401.

Full text

Abstract:

The blood vessels and nerve trees consist of tubular objects interconnected into a complex tree- or web-like structure that has a range of structural scale 5 μm diameter capillaries to 3 cm aorta. This large-scale range presents two major problems; one is just making the measurements, and the other is the exponential increase of component numbers with decreasing scale. With the remarkable increase in the volume imaged by, and resolution of, modern day 3D imagers, it is almost impossible to make manual tracking of the complex multiscale parameters from those large image data sets. In addition, the manual tracking is quite subjective and unreliable. We propose a solution for automation of an adaptive nonsupervised system for tracking tubular objects based on multiscale framework and use of Hessian-based object shape detector incorporating National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) image processing libraries.

APA, Harvard, Vancouver, ISO, and other styles

45

Bégin, Steve, Olivier Dupont-Therrien, Erik Bélanger, et al. "Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue." Biomedical Optics Express 5, no. 12 (2014): 4145. http://dx.doi.org/10.1364/boe.5.004145.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Balsiger, F., C. Steindel, M. Arn, et al. "P13. Semi-automatic, machine-learning based segmentation of peripheral nerves for quantitative morphometry: Comparison of low- and high-resolution MR neurography." Clinical Neurophysiology 129, no. 8 (2018): e70-e71. http://dx.doi.org/10.1016/j.clinph.2018.04.655.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Hu, Zhihong, Michael D. Abràmoff, Young H. Kwon, Kyungmoo Lee, and Mona K. Garvin. "Automated Segmentation of Neural Canal Opening and Optic Cup in 3D Spectral Optical Coherence Tomography Volumes of the Optic Nerve Head." Investigative Opthalmology & Visual Science 51, no. 11 (2010): 5708. http://dx.doi.org/10.1167/iovs.09-4838.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Saidha, Shiv, Stephanie B. Syc, Mary K. Durbin, et al. "Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness." Multiple Sclerosis Journal 17, no. 12 (2011): 1449–63. http://dx.doi.org/10.1177/1352458511418630.

Full text

Abstract:

Background: Post-mortem analyses of multiple sclerosis (MS) eyes demonstrate prominent retinal neuronal ganglion cell layer (GCL) loss, in addition to related axonal retinal nerve fiber layer (RNFL) loss. Despite this, clinical correlations of retinal neuronal layers remain largely unexplored in MS. Objectives: To determine if MS patients exhibit in vivo retinal neuronal GCL loss, deeper retinal neuronal loss, and investigate correlations between retinal layer thicknesses, MS clinical subtype and validated clinical measures. Methods: Cirrus HD-optical coherence tomography (OCT), utilizing automated intra-retinal layer segmentation, was performed in 132 MS patients and 78 healthy controls. MS classification, Expanded Disability Status Scale (EDSS) and visual function were recorded in study subjects. Results: GCL+inner plexiform layer (GCIP) was thinner in relapsing–remitting MS (RRMS; n = 96, 71.6 µm), secondary progressive MS (SPMS; n = 20, 66.4 µm) and primary progressive MS (PPMS; n = 16, 74.1 µm) than in healthy controls (81.8 µm; p < 0.001 for all). GCIP thickness was most decreased in SPMS, and although GCIP thickness correlated significantly with disease duration, after adjusting for this, GCIP thickness remained significantly lower in SPMS than RRMS. GCIP thickness correlated significantly, and better than RNFL thickness, with EDSS, high-contrast, 2.5% low-contrast and 1.25% low-contrast letter acuity in MS. 13.6% of patients also demonstrated inner or outer nuclear layer thinning. Conclusions: OCT segmentation demonstrates in vivo GCIP thinning in all MS subtypes. GCIP thickness demonstrates better structure-function correlations (with vision and disability) in MS than RNFL thickness. In addition to commonly observed RNFL/GCIP thinning, retinal inner and outer nuclear layer thinning occur in MS.

APA, Harvard, Vancouver, ISO, and other styles

49

Cornelissen, Frans, Peter Verstraelen, Tobias Verbeke, et al. "Quantitation of Chronic and Acute Treatment Effects on Neuronal Network Activity Using Image and Signal Analysis." Journal of Biomolecular Screening 18, no. 7 (2013): 807–19. http://dx.doi.org/10.1177/1087057113486518.

Full text

Abstract:

Upon maturation, primary neuronal cultures form an interconnected network based on neurite outgrowth and synaptogenesis in which spontaneous electrical activity arises. Measurement of network activity allows quantification of neuronal health and maturation. A fluorescent indicator was used to monitor secondary calcium influxes after the occurrence of action potentials, allowing us to examine activity of hippocampal cultures via confocal live cell imaging. Subsequently, nuclear staining with DAPI allows accurate cell segmentation. To analyze the calcium recording in a robust, observer-independent manner, we implemented an automated image- and signal-processing algorithm and validated it against a visual, interactive procedure. Both methods yielded similar results on the emergence of synchronized activity and allowed robust quantitative measurement of acute and chronic modulation of drugs on network activity. Both the number of days in vitro (DIV) and neutralization of nerve growth factor (NGF) have a significant effect on synchronous burst frequency and correlation. Acute effects are demonstrated using 5-HT (serotonin) and ethylene glycol tetra-acetic acid. Automated analysis allowed measuring additional features, such as peak decay times and bursting frequency of individual neurons. Based on neuronal cell cultures in 96-well plates and accurate calcium recordings, the analysis method allows development of an integrated high-content screening assay. Because molecular biological techniques can be applied to assess the influence of genes on network activity, it is applicable for neurotoxicity or neurotrophics screening as well as development of in vitro disease models via, for example, pharmacologic manipulation or RNAi.

APA, Harvard, Vancouver, ISO, and other styles

50

Raphael, David T., Diane McIntee, Jay S. Tsuruda, Patrick Colletti, and Ray Tatevossian. "Frontal Slab Composite Magnetic Resonance Neurography of the Brachial Plexus." Anesthesiology 103, no. 6 (2005): 1218–24. http://dx.doi.org/10.1097/00000542-200512000-00017.

Full text

Abstract:

Background Magnetic resonance neurography (MRN) is an imaging method by which nerves can be selectively highlighted. Using commercial software, the authors explored a variety of approaches to develop a three-dimensional volume-rendered MRN image of the entire brachial plexus and used it to evaluate the accuracy of infraclavicular block approaches. Methods With institutional review board approval, MRN of the brachial plexus was performed in 10 volunteer subjects. MRN imaging was performed on a GE 1.5-tesla magnetic resonance scanner (General Electric Healthcare Technologies, Waukesha, WI) using a phased array torso coil. Coronal STIR and T1 oblique sagittal sequences of the brachial plexus were obtained. Multiple software programs were explored for enhanced display and manipulation of the composite magnetic resonance images. The authors developed a frontal slab composite approach that allows single-frame reconstruction of a three-dimensional volume-rendered image of the entire brachial plexus. Automatic segmentation was supplemented by manual segmentation in nearly all cases. For each of three infraclavicular approaches (posteriorly directed needle below midclavicle, infracoracoid, or caudomedial to coracoid), the targeting error was measured as the distance from the MRN plexus midpoint to the approach-targeted site. Results Composite frontal slabs (coronal views), which are single-frame three-dimensional volume renderings from image-enhanced two-dimensional frontal view projections of the underlying coronal slices, were created. The targeting errors (mean +/- SD) for the approaches-midclavicle, infracoracoid, caudomedial to coracoid-were 0.43 +/- 0.67, 0.99 +/- 1.22, and 0.65 +/- 1.14 cm, respectively. Conclusion Image-processed three-dimensional volume-rendered MNR scans, which allow visualization of the entire brachial plexus within a single composite image, have educational value in illustrating the complexity and individual variation of the plexus. Suggestions for improved guidance during infraclavicular block procedures are presented.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Automatic nerve segmentation'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles