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Journal articles on the topic 'Multi-Atlas de segmentation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kim, Sally Ji Who, Seongho Seo, Hyeon Sik Kim, et al. "Multi-atlas cardiac PET segmentation." Physica Medica 58 (February 2019): 32–39. http://dx.doi.org/10.1016/j.ejmp.2019.01.003.

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2

Asman, Andrew J., Lola B. Chambless, Reid C. Thompson, and Bennett A. Landman. "Out-of-atlas likelihood estimation using multi-atlas segmentation." Medical Physics 40, no. 4 (2013): 043702. http://dx.doi.org/10.1118/1.4794478.

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3

Li, Yuan, Fu Cang Jia, Xiao Dong Zhang, Cheng Huang, and Huo Ling Luo. "Local Patch Similarity Ranked Voxelwise STAPLE on Magnetic Resonance Image Hippocampus Segmentation." Applied Mechanics and Materials 333-335 (July 2013): 1065–70. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1065.

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The segmentation and labeling of sub-cortical structures of interest are important tasks for the assessment of morphometric features in quantitative magnetic resonance (MR) image analysis. Recently, multi-atlas segmentation methods with statistical fusion strategy have demonstrated high accuracy in hippocampus segmentation. While, most of the segmentations rarely consider spatially variant model and reserve all segmentations. In this study, we propose a novel local patch-based and ranking strategy for voxelwise atlas selection to extend the original Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The local ranking strategy is based on the metric of normalized cross correlation (NCC). Unlike its predecessors, this method estimates the fusion of each voxel patch-by-patch and makes use of gray image features as a prior. Validation results on 33 pairs of hippocampus MR images show good performance on the segmentation of hippocampus.

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4

Karasawa, Ken’ichi, Masahiro Oda, Takayuki Kitasaka, et al. "Multi-atlas pancreas segmentation: Atlas selection based on vessel structure." Medical Image Analysis 39 (July 2017): 18–28. http://dx.doi.org/10.1016/j.media.2017.03.006.

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5

Hoang Duc, Albert K., Marc Modat, Kelvin K. Leung, et al. "Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation." PLoS ONE 8, no. 8 (2013): e70059. http://dx.doi.org/10.1371/journal.pone.0070059.

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6

Hongzhi Wang, J. W. Suh, S. R. Das, J. B. Pluta, C. Craige, and P. A. Yushkevich. "Multi-Atlas Segmentation with Joint Label Fusion." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 3 (2013): 611–23. http://dx.doi.org/10.1109/tpami.2012.143.

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7

Sharma, Manish Kumar, Mainak Jas, Vikrant Karale, Anup Sadhu, and Sudipta Mukhopadhyay. "Mammogram segmentation using multi-atlas deformable registration." Computers in Biology and Medicine 110 (July 2019): 244–53. http://dx.doi.org/10.1016/j.compbiomed.2019.06.001.

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8

Antonelli, Michela, M. Jorge Cardoso, Edward W. Johnston, et al. "GAS: A genetic atlas selection strategy in multi-atlas segmentation framework." Medical Image Analysis 52 (February 2019): 97–108. http://dx.doi.org/10.1016/j.media.2018.11.007.

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9

Zhao, Tingting, and Dan Ruan. "Two-stage atlas subset selection in multi-atlas based image segmentation." Medical Physics 42, no. 6Part1 (2015): 2933–41. http://dx.doi.org/10.1118/1.4921138.

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10

Huo, Yuankai, Jiaqi Liu, Zhoubing Xu, et al. "Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation." IEEE Transactions on Biomedical Engineering 65, no. 2 (2018): 336–43. http://dx.doi.org/10.1109/tbme.2017.2764752.

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11

Zhu, Hancan, and Guanghua He. "Joint Neighboring Coding with a Low-Rank Constraint for Multi-Atlas Based Image Segmentation." Journal of Medical Imaging and Health Informatics 10, no. 2 (2020): 310–15. http://dx.doi.org/10.1166/jmihi.2020.2884.

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Multi-atlas methods have been successful for solving many medical image segmentation problems. Under the multi-atlas segmentation framework, labels of atlases are first propagated to the target image space with the deformation fields generated by registering atlas images onto a target image, and then these labels are fused to obtain the final segmentation. While many label fusion strategies have been developed, weighting based label fusion methods have attracted considerable attention. In this paper, we first present a unified framework for weighting based label fusion methods. Under this unified framework, we find that most of recent developed weighting based label fusion methods jointly consider the pair-wise dependency between atlases. However, they independently label voxels to be segmented, ignoring their neighboring spatial structure that might be informative for obtaining robust segmentation results for noisy images. Taking into consideration of potential correlation among neighboring voxels to be segmented, we propose a joint coding method (JCM) with a low-rank constraint for the multi-atlas based image segmentation in a general framework that unifies existing weighting based label fusion methods. The method has been validated for segmenting hippocampus from MR images. It is demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods, especially when the quality of images is poor.

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12

Iglesias, Juan Eugenio, and Mert R. Sabuncu. "Multi-atlas segmentation of biomedical images: A survey." Medical Image Analysis 24, no. 1 (2015): 205–19. http://dx.doi.org/10.1016/j.media.2015.06.012.

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13

Rohlfing, Torsten, and Calvin R. Maurer. "Multi-classifier framework for atlas-based image segmentation." Pattern Recognition Letters 26, no. 13 (2005): 2070–79. http://dx.doi.org/10.1016/j.patrec.2005.03.017.

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14

Huo, Yuankai, Andrew J. Plassard, Aaron Carass, et al. "Consistent cortical reconstruction and multi-atlas brain segmentation." NeuroImage 138 (September 2016): 197–210. http://dx.doi.org/10.1016/j.neuroimage.2016.05.030.

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15

Yang, J., B. Beadle, A. Garden, P. Balter, and L. Court. "TH-C-WAB-04: Atlas Ranking and Selection for Multi-Atlas Segmentation." Medical Physics 40, no. 6Part32 (2013): 537. http://dx.doi.org/10.1118/1.4815761.

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16

Pirozzi, S., M. Horvat, A. Nelson, and J. Piper. "Atlas-based Segmentation: Evaluation of a Multi-atlas Approach for Prostate Cancer." International Journal of Radiation Oncology*Biology*Physics 84, no. 3 (2012): S799. http://dx.doi.org/10.1016/j.ijrobp.2012.07.2137.

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17

Shi, Yonggang, Xueping Zhang, and Zhiwen Liu. "Automatic segmentation of hippocampal subfields based on multi-atlas image segmentation techniques." Journal of Electronics (China) 31, no. 2 (2014): 121–28. http://dx.doi.org/10.1007/s11767-014-3183-x.

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18

Jia, Hongjun, Pew-Thian Yap, and Dinggang Shen. "Iterative multi-atlas-based multi-image segmentation with tree-based registration." NeuroImage 59, no. 1 (2012): 422–30. http://dx.doi.org/10.1016/j.neuroimage.2011.07.036.

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19

Asman, Andrew J., and Bennett A. Landman. "Non-local statistical label fusion for multi-atlas segmentation." Medical Image Analysis 17, no. 2 (2013): 194–208. http://dx.doi.org/10.1016/j.media.2012.10.002.

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20

Bustamante, Mariana, Vikas Gupta, Daniel Forsberg, Carl-Johan Carlhäll, Jan Engvall, and Tino Ebbers. "Automated multi-atlas segmentation of cardiac 4D flow MRI." Medical Image Analysis 49 (October 2018): 128–40. http://dx.doi.org/10.1016/j.media.2018.08.003.

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21

Alvén, Jennifer, Alexander Norlén, Olof Enqvist, and Fredrik Kahl. "Überatlas: Fast and robust registration for multi-atlas segmentation." Pattern Recognition Letters 80 (September 2016): 249–55. http://dx.doi.org/10.1016/j.patrec.2016.05.001.

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22

Nouranian, Saman, S. Sara Mahdavi, Ingrid Spadinger, William J. Morris, Septimu E. Salcudean, and Purang Abolmaesumi. "A Multi-Atlas-Based Segmentation Framework for Prostate Brachytherapy." IEEE Transactions on Medical Imaging 34, no. 4 (2015): 950–61. http://dx.doi.org/10.1109/tmi.2014.2371823.

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23

Zhu, Hancan, Hewei Cheng, Xuesong Yang, and Yong Fan. "Metric Learning for Multi-atlas based Segmentation of Hippocampus." Neuroinformatics 15, no. 1 (2016): 41–50. http://dx.doi.org/10.1007/s12021-016-9312-y.

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24

Shi, Changfa, Min Xian, Xiancheng Zhou, Haotian Wang, and Heng-Da Cheng. "Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation." Medical Image Analysis 73 (October 2021): 102152. http://dx.doi.org/10.1016/j.media.2021.102152.

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25

Cárdenas-Peña, David, Eduardo Fernández, José M. Ferrández-Vicente, and German Castellanos-Domínguez. "Multi-atlas label fusion by using supervised local weighting for brain image segmentation." TecnoLógicas 20, no. 39 (2017): 209–25. http://dx.doi.org/10.22430/22565337.724.

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The automatic segmentation of interest structures is devoted to the morphological analysis of brain magnetic resonance imaging volumes. It demands significant efforts due to its complicated shapes and since it lacks contrast between tissues and intersubject anatomical variability. One aspect that reduces the accuracy of the multi-atlasbased segmentation is the label fusion assumption of one-to-one correspondences between targets and atlas voxels. To improve the performance of brain image segmentation, label fusion approaches include spatial and intensity information by using voxel-wise weighted voting strategies. Although the weights are assessed for a predefined atlas set, they are not very efficient for labeling intricate structures since most tissue shapes are not uniformly distributed in the images. This paper proposes a methodology of voxel-wise feature extraction based on the linear combination of patch intensities. As far as we are concerned, this is the first attempt to locally learn the features by maximizing the centered kernel alignment function. Our methodology aims to build discriminative representations, deal with complex structures, and reduce the image artifacts. The result is an enhanced patch-based segmentation of brain images. For validation, the proposed brain image segmentation approach is compared against Bayesian-based and patch-wise label fusion on three different brain image datasets. In terms of the determined Dice similarity index, our proposal shows the highest segmentation accuracy (90.3% on average); it presents sufficient artifact robustness, and provides suitable repeatability of the segmentation results.

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26

Aljabar, P., R. A. Heckemann, A. Hammers, J. V. Hajnal, and D. Rueckert. "Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy." NeuroImage 46, no. 3 (2009): 726–38. http://dx.doi.org/10.1016/j.neuroimage.2009.02.018.

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27

Zhao, T., and D. Ruan. "SU-E-J-128: Two-Stage Atlas Selection in Multi-Atlas-Based Image Segmentation." Medical Physics 42, no. 6Part9 (2015): 3294. http://dx.doi.org/10.1118/1.4924214.

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28

Zhou, R., J. Yang, T. Pan, et al. "SU-E-J-129: Atlas Development for Cardiac Automatic Contouring Using Multi-Atlas Segmentation." Medical Physics 42, no. 6Part9 (2015): 3294. http://dx.doi.org/10.1118/1.4924215.

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29

Asman, Andrew J., Frederick W. Bryan, Seth A. Smith, Daniel S. Reich, and Bennett A. Landman. "Groupwise multi-atlas segmentation of the spinal cord’s internal structure." Medical Image Analysis 18, no. 3 (2014): 460–71. http://dx.doi.org/10.1016/j.media.2014.01.003.

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30

Bai, Wenjia, Wenzhe Shi, Christian Ledig, and Daniel Rueckert. "Multi-atlas segmentation with augmented features for cardiac MR images." Medical Image Analysis 19, no. 1 (2015): 98–109. http://dx.doi.org/10.1016/j.media.2014.09.005.

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31

Wolz, Robin, Chengwen Chu, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, and Daniel Rueckert. "Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation." IEEE Transactions on Medical Imaging 32, no. 9 (2013): 1723–30. http://dx.doi.org/10.1109/tmi.2013.2265805.

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32

Zhu, Hancan, Ehsan Adeli, Feng Shi, and Dinggang Shen. "FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation." Neuroinformatics 18, no. 2 (2020): 319–31. http://dx.doi.org/10.1007/s12021-019-09448-5.

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33

Chu, Chengwen, Junjie Bai, Xiaodong Wu, and Guoyan Zheng. "MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images." Medical Image Analysis 26, no. 1 (2015): 173–84. http://dx.doi.org/10.1016/j.media.2015.08.011.

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34

Lin, Xiangbo, and Xiaoxi Li. "Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 5 (2019): 443–52. http://dx.doi.org/10.2174/1573405614666180817125454.

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Background: This review aims to identify the development of the algorithms for brain tissue and structure segmentation in MRI images. Discussion: Starting from the results of the Grand Challenges on brain tissue and structure segmentation held in Medical Image Computing and Computer-Assisted Intervention (MICCAI), this review analyses the development of the algorithms and discusses the tendency from multi-atlas label fusion to deep learning. The intrinsic characteristics of the winners’ algorithms on the Grand Challenges from the year 2012 to 2018 are analyzed and the results are compared carefully. Conclusion: Although deep learning has got higher rankings in the challenge, it has not yet met the expectations in terms of accuracy. More effective and specialized work should be done in the future.

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35

Zhao, T., and D. Ruan. "TU-CD-BRA-05: Atlas Selection for Multi-Atlas-Based Image Segmentation Using Surrogate Modeling." Medical Physics 42, no. 6Part32 (2015): 3606. http://dx.doi.org/10.1118/1.4925602.

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36

Lötjönen, Jyrki MP, Robin Wolz, Juha R. Koikkalainen, et al. "Fast and robust multi-atlas segmentation of brain magnetic resonance images." NeuroImage 49, no. 3 (2010): 2352–65. http://dx.doi.org/10.1016/j.neuroimage.2009.10.026.

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37

Huo, Jie, Jonathan Wu, Jiuwen Cao, and Guanghui Wang. "Supervoxel based method for multi-atlas segmentation of brain MR images." NeuroImage 175 (July 2018): 201–14. http://dx.doi.org/10.1016/j.neuroimage.2018.04.001.

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38

Koch, Lisa Margret, Martin Rajchl, Wenjia Bai, et al. "Multi-Atlas Segmentation Using Partially Annotated Data: Methods and Annotation Strategies." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 7 (2018): 1683–96. http://dx.doi.org/10.1109/tpami.2017.2711020.

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39

Dong, Pei, Yanrong Guo, Yue Gao, Peipeng Liang, Yonghong Shi, and Guorong Wu. "Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning." IEEE Transactions on Neural Networks and Learning Systems 31, no. 8 (2020): 3061–72. http://dx.doi.org/10.1109/tnnls.2019.2935184.

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40

Sun, Liang, Chen Zu, Wei Shao, Junye Guang, Daoqiang Zhang, and Mingxia Liu. "Reliability-based robust multi-atlas label fusion for brain MRI segmentation." Artificial Intelligence in Medicine 96 (May 2019): 12–24. http://dx.doi.org/10.1016/j.artmed.2019.03.004.

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41

Wang, Yi, Yi Zhao, Zhe Guo, Min Qi, Yangyu Fan, and Hongying Meng. "Diffusion Tensor Image segmentation based on multi-atlas Active Shape Model." Multimedia Tools and Applications 78, no. 24 (2019): 34231–46. http://dx.doi.org/10.1007/s11042-019-08051-9.

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42

Alchatzidis, Stavros, Aristeidis Sotiras, Evangelia I. Zacharaki, and Nikos Paragios. "A Discrete MRF Framework for Integrated Multi-Atlas Registration and Segmentation." International Journal of Computer Vision 121, no. 1 (2016): 169–81. http://dx.doi.org/10.1007/s11263-016-0925-2.

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43

Pirozzi, S., M. Horvat, J. Piper, and A. Nelson. "SU-E-J-106: Atlas-Based Segmentation: Evaluation of a Multi-Atlas Approach for Lung Cancer." Medical Physics 39, no. 6Part7 (2012): 3677. http://dx.doi.org/10.1118/1.4734942.

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44

Zaffino, P., K. Fritscher, M. Peroni, M. F. Spadea, R. Schubert, and G. Sharp. "OC-0180: Atlas selection strategies for multi atlas based segmentation algorithm for head and neck radiotherapy." Radiotherapy and Oncology 111 (2014): S70—S71. http://dx.doi.org/10.1016/s0167-8140(15)30285-1.

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45

Ye, Chenfei, Ting Ma, Dan Wu, Can Ceritoglu, Michael I. Miller, and Susumu Mori. "Atlas pre-selection strategies to enhance the efficiency and accuracy of multi-atlas brain segmentation tools." PLOS ONE 13, no. 7 (2018): e0200294. http://dx.doi.org/10.1371/journal.pone.0200294.

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46

Langerak, T. R., U. A. van der Heide, A. N. T. J. Kotte, F. F. Berendsen, and J. P. W. Pluim. "Improving label fusion in multi-atlas based segmentation by locally combining atlas selection and performance estimation." Computer Vision and Image Understanding 130 (January 2015): 71–79. http://dx.doi.org/10.1016/j.cviu.2014.09.004.

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47

Su, Jason H., Francis T. Thomas, Willard S. Kasoff, et al. "Thalamus Optimized Multi Atlas Segmentation (THOMAS): fast, fully automated segmentation of thalamic nuclei from structural MRI." NeuroImage 194 (July 2019): 272–82. http://dx.doi.org/10.1016/j.neuroimage.2019.03.021.

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48

Wang, Xue, Ali Ghayoor, Andrew Novicki, Sean Holmes, John Seibyl, and Jacob Hesterman. "[P4-266]: APPLICATION OF A MULTI-ATLAS SEGMENTATION TOOL TO HIPPOCAMPUS, VENTRICLE AND WHOLE BRAIN SEGMENTATION." Alzheimer's & Dementia 13, no. 7S_Part_28 (2017): P1385—P1386. http://dx.doi.org/10.1016/j.jalz.2017.06.2135.

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49

Nie, Jingxin, and Dinggang Shen. "Automated Segmentation of Mouse Brain Images Using Multi-Atlas Multi-ROI Deformation and Label Fusion." Neuroinformatics 11, no. 1 (2012): 35–45. http://dx.doi.org/10.1007/s12021-012-9163-0.

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50

Shi Yuexiang, 石跃祥, and 陈才 Chen Cai. "Non-Rigid Registration Segmentation Algorithm Based on Optimal Atlas Multi-Model Image." Acta Optica Sinica 39, no. 4 (2019): 0410002. http://dx.doi.org/10.3788/aos201939.0410002.

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