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Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for training deep learning based segmentation models.
“Advances in functional and structural mr image analysis and implementation as fsl,”
Stephen M Smith, Mark Jenkinson, Mark W Woolrich, Christian F Beckmann, Timothy EJ Behrens, Heidi Johansen-Berg, Peter R Bannister, Marilena De Luca, Ivana Drobnjak, David E Flitney, et al., · 2004
Earlier work this paper cites.
“An automated tool for detection of flair-hyperintense white-matter lesions in multiple sclerosis,”
Paul Schmidt, Christian Gaser, Milan Arsic, Dorothea Buck, Annette Förschler, Achim Berthele, Muna Hoshi, Rüdiger Ilg, Volker J Schmid, Claus Zimmer, et al., · 2012
Earlier work this paper cites.
“3d blob based brain tumor detection and segmentation in mr images,”
Chen-Ping Yu, Guilherme Ruppert, Robert Collins, Dan Nguyen, Alexandre Falcao, and Yanxi Liu, · 2014
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“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2014
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“Dcan: deep contour-aware networks for accurate gland segmentation,”
Hao Chen, Xiaojuan Qi, Lequan Yu, and Pheng-Ann Heng, · 2016
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“3d u-net: learning dense volumetric segmentation from sparse annotation,”
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger, · 2016
Earlier work this paper cites.
“Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection,”
Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Sanjay P Prabhu, Simon K Warfield, and Ali Gholipour, · 2018
Cited alongside, same era.
“Besnet: boundary-enhanced segmentation of cells in histopathological images,”
Hirohisa Oda, Holger R Roth, Kosuke Chiba, Jure Sokolić, Takayuki Kitasaka, Masahiro Oda, Akinari Hinoki, Hiroo Uchida, Julia A Schnabel, and Kensaku Mori, · 2018
Cited alongside, same era.
“Rsanet: Recurrent slice-wise attention network for multiple sclerosis lesion segmentation,”
Hang Zhang, Jinwei Zhang, Qihao Zhang, Jeremy Kim, Shun Zhang, Susan A Gauthier, Pascal Spincemaille, Thanh D Nguyen, Mert Sabuncu, and Yi Wang, · 2019
Cited alongside, same era.
“Multiple sclerosis lesion segmentation with tiramisu and 2.5 d stacked slices,”
Huahong Zhang, Alessandra M Valcarcel, Rohit Bakshi, Renxin Chu, Francesca Bagnato, Russell T Shinohara, Kilian Hett, and Ipek Oguz, · 2019
Cited alongside, same era.
“Multi-branch convolutional neural network for multiple sclerosis lesion segmentation,”
“Boundary loss for highly unbalanced segmentation,”
Hoel Kervadec, Jihene Bouchtiba, Christian Desrosiers, Eric Granger, Jose Dolz, and Ismail Ben Ayed, · 2019
Later among the works it cites.
“Reducing the hausdorff distance in medical image segmentation with convolutional neural networks,”
Davood Karimi and Septimiu E Salcudean, · 2019
Later among the works it cites.
“Shape-aware organ segmentation by predicting signed distance maps,”
Yuan Xue, Hui Tang, Zhi Qiao, Guanzhong Gong, Yong Yin, Zhen Qian, Chao Huang, Wei Fan, and Xiaolei Huang, · 2019
Later among the works it cites.
“Pytorch: An imperative style, high-performance deep learning library,”
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al., · 2019
Later among the works it cites.
“Efficient folded attention for 3d medical image reconstruction and segmentation,”
Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D Nguyen, and Yi Wang, · 2020
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Shahab Aslani, Michael Dayan, Loredana Storelli, Massimo Filippi, Vittorio Murino, Maria A Rocca, and Diego Sona, · 2019
Cited alongside, same era.
“Cross attention densely connected networks for multiple sclerosis lesion segmentation,”
Beibei Hou, Guixia Kang, Xin Xu, and Chuan Hu, · 2019
Cited alongside, same era.
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