Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
This work addresses the scalability issue in brain mapping for neuroscience and medical imaging, though it is incremental as it builds on existing CNN models with a self-supervised pre-training approach.
The paper tackled the challenge of automatic cytoarchitectonic segmentation of human brain areas, which suffers from limited expert annotations, by proposing a self-supervised Siamese network pre-trained on predicting 3D distances between patches, resulting in significantly better segmentations compared to random initialization.
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.