Relevant bibliographies by topics / NnU-Net

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'NnU-Net'

Author: Grafiati
Published: 16 July 2021
Last updated: 10 February 2022

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Journal articles on the topic "NnU-Net"

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Savjani, Ricky. "nnU-Net: Further Automating Biomedical Image Autosegmentation." Radiology: Imaging Cancer 3, no. 1 (2021): e209039. http://dx.doi.org/10.1148/rycan.2021209039.

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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, et al. "The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images." Diagnostics 11, no. 9 (2021): 1690. http://dx.doi.org/10.3390/diagnostics11091690.

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Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manu
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Zhang, Guobin, Zhiyong Yang, Bin Huo, Shude Chai, and Shan Jiang. "Multiorgan segmentation from partially labeled datasets with conditional nnU-Net." Computers in Biology and Medicine 136 (September 2021): 104658. http://dx.doi.org/10.1016/j.compbiomed.2021.104658.

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Lian, Luya, Tianer Zhu, Fudong Zhu, and Haihua Zhu. "Deep Learning for Caries Detection and Classification." Diagnostics 11, no. 9 (2021): 1672. http://dx.doi.org/10.3390/diagnostics11091672.

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Objectives: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. Methods: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then e
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Abel, Lorraine, Jakob Wasserthal, Thomas Weikert, et al. "Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning." Diagnostics 11, no. 5 (2021): 901. http://dx.doi.org/10.3390/diagnostics11050901.

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Pancreatic cystic lesions (PCL) are a frequent and underreported incidental finding on CT scans and can transform into neoplasms with devastating consequences. We developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of PCL on CTs. A total of 543 cysts on 221 abdominal CTs were manually segmented in 3D by a radiology resident in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a two-step nnU-Net for detection with the performance assessed depending on lesions’ volume and location
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Huo, Lu, Xiaoxin Hu, Qin Xiao, Yajia Gu, Xu Chu, and Luan Jiang. "Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images." Magnetic Resonance Imaging 82 (October 2021): 31–41. http://dx.doi.org/10.1016/j.mri.2021.06.017.

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Heidenreich, Julius F., Tobias Gassenmaier, Markus J. Ankenbrand, Thorsten A. Bley, and Tobias Wech. "Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction." European Journal of Radiology 141 (August 2021): 109817. http://dx.doi.org/10.1016/j.ejrad.2021.109817.

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Tahuk, Paulus Klau, Agustinus Agung Dethan, and Stefanus Sio. "ENERGY AND NITROGEN BALANCE OF MALE BALI CATTLE FATTENED BY GREEN FEED IN SMALLHOLDER FARMS." Journal of Tropical Animal Science and Technology 2, no. 1 (2020): 23–36. http://dx.doi.org/10.32938/jtast.v2i1.590.

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The experiment was conducted for 3 months from March to June 2013 using nine (9) males Bali Cattle ages 2,5 - 3,5 or an average 3.0 years old based on teeth estimated with initial body weight range is 227-290 kg or an average of 257.40±23,60 kg in the Fattening Stalls, Bero Sembada Farmers Group, Laen Manen Sub District, Belu Regency, East Nusa Tenggara. This research be adapted to the practice of ranchers in fattened of cattle that includes management of feeding, housing, and health. Type of feed given during the study was Centrosema pubences, Clitoria ternatea, jerami Zea mays segar, Pennise
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Jung, Seok-Ki, Ho-Kyung Lim, Seungjun Lee, Yongwon Cho, and In-Seok Song. "Deep Active Learning for Automatic Segmentation of Maxillary Sinus Lesions Using a Convolutional Neural Network." Diagnostics 11, no. 4 (2021): 688. http://dx.doi.org/10.3390/diagnostics11040688.

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The aim of this study was to segment the maxillary sinus into the maxillary bone, air, and lesion, and to evaluate its accuracy by comparing and analyzing the results performed by the experts. We randomly selected 83 cases of deep active learning. Our active learning framework consists of three steps. This framework adds new volumes per step to improve the performance of the model with limited training datasets, while inferring automatically using the model trained in the previous step. We determined the effect of active learning on cone-beam computed tomography (CBCT) volumes of dental with o
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Bouget, David, Roelant S. Eijgelaar, André Pedersen, et al. "Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task." Cancers 13, no. 18 (2021): 4674. http://dx.doi.org/10.3390/cancers13184674.

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For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two rece
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Dissertations / Theses on the topic "NnU-Net"

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Bergsneider, Andres. "Biological Semantic Segmentation on CT Medical Images for Kidney Tumor Detection Using nnU-Net Framework." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2298.

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Healthcare systems are constantly challenged with bottlenecks due to human-reliant operations, such as analyzing medical images. High precision and repeatability is necessary when performing a diagnostics on patients with tumors. Throughout the years an increasing number of advancements have been made using various machine learning algorithms for the detection of tumors helping to fast track diagnosis and treatment decisions. “Black Box” systems such as the complex deep learning networks discussed in this paper rely heavily on hyperparameter optimization in order to obtain the most ideal perfo
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Book chapters on the topic "NnU-Net"

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Isensee, Fabian, Paul F. Jäger, Peter M. Full, Philipp Vollmuth, and Klaus H. Maier-Hein. "nnU-Net for Brain Tumor Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72087-2_11.

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Isensee, Fabian, Jens Petersen, Andre Klein, et al. "Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-25326-4_7.

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Gonzalez, Camila, Karol Gotkowski, Andreas Bucher, Ricarda Fischbach, Isabel Kaltenborn, and Anirban Mukhopadhyay. "Detecting When Pre-trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87234-2_29.

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Xie, Juanying, and Ying Peng. "The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel ‘Squeeze & Excitation’ Blocks." In Head and Neck Tumor Segmentation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67194-5_3.

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Conference papers on the topic "NnU-Net"

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Graham-Knight, J. B., A. Djavadifar, Dr P. Lasserre, and H. Najjaran. "Applying nnU-Net to the KiTS19 Grand Challenge." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.015.

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