%0 Conference Paper %1 10.1007/978-3-030-00931-1_76 %A Spitzer, Hannah %A Kiwitz, Kai %A Amunts, Katrin %A Harmeling, Stefan %A Dickscheid, Timo %B Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018 %C Cham %D 2018 %E Frangi, Alejandro F. %E Schnabel, Julia A. %E Davatzikos, Christos %E Alberola-López, Carlos %E Fichtinger, Gabor %I Springer International Publishing %K myown %P 663--671 %T Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks %X Cytoarchitectonic parcellations of the human brain serve as anatomical references in multimodal atlas frameworks. They are based on analysis of cell-body stained histological sections and the identification of borders between brain areas. The de-facto standard involves a semi-automatic, reproducible border detection, but does not scale with high-throughput imaging in large series of sections at microscopical resolution. Automatic parcellation, however, is extremely challenging due to high variation in the data, and the need for a large field of view at microscopic resolution. The performance of a recently proposed Convolutional Neural Network model that addresses this problem especially suffers from the naturally limited amount of expert annotations for training. To circumvent this limitation, we propose to pre-train neural networks on a self-supervised auxiliary task, predicting the 3D distance between two patches sampled from the same brain. Compared to a random initialization, fine-tuning from these networks results in significantly better segmentations. We show that the self-supervised model has implicitly learned to distinguish several cortical brain areas -- a strong indicator that the proposed auxiliary task is appropriate for cytoarchitectonic mapping. %@ 978-3-030-00931-1

@inproceedings{10.1007/978-3-030-00931-1_76, abstract = {Cytoarchitectonic parcellations of the human brain serve as anatomical references in multimodal atlas frameworks. They are based on analysis of cell-body stained histological sections and the identification of borders between brain areas. The de-facto standard involves a semi-automatic, reproducible border detection, but does not scale with high-throughput imaging in large series of sections at microscopical resolution. Automatic parcellation, however, is extremely challenging due to high variation in the data, and the need for a large field of view at microscopic resolution. The performance of a recently proposed Convolutional Neural Network model that addresses this problem especially suffers from the naturally limited amount of expert annotations for training. To circumvent this limitation, we propose to pre-train neural networks on a self-supervised auxiliary task, predicting the 3D distance between two patches sampled from the same brain. Compared to a random initialization, fine-tuning from these networks results in significantly better segmentations. We show that the self-supervised model has implicitly learned to distinguish several cortical brain areas -- a strong indicator that the proposed auxiliary task is appropriate for cytoarchitectonic mapping.}, added-at = {2019-04-09T11:38:55.000+0200}, address = {Cham}, author = {Spitzer, Hannah and Kiwitz, Kai and Amunts, Katrin and Harmeling, Stefan and Dickscheid, Timo}, biburl = {https://www.bibsonomy.org/bibtex/2be810f7e010a2a6dca3dbe9e4d78a1bf/dickscheid}, booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018}, description = {Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks | SpringerLink}, editor = {Frangi, Alejandro F. and Schnabel, Julia A. and Davatzikos, Christos and Alberola-L{\'o}pez, Carlos and Fichtinger, Gabor}, interhash = {31a8619e74245151a58ed8dcd1175bb5}, intrahash = {be810f7e010a2a6dca3dbe9e4d78a1bf}, isbn = {978-3-030-00931-1}, keywords = {myown}, pages = {663--671}, publisher = {Springer International Publishing}, timestamp = {2019-04-09T15:39:51.000+0200}, title = {Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks}, year = 2018 }