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arXiv · 1806.05104

Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks

Hannah Spitzer·Kai Kiwitz·Katrin Amunts·Stefan Harmeling·Timo Dickscheid
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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.

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Hannah Spitzer, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, Timo Dickscheid. 2018-06-13. Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks. https://doi.org/10.1007/978-3-030-00931-1_76

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