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

arXiv: https://arxiv.org/abs/1806.05104 · HTML: https://mlsanity.com/paper/1806.05104/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 30 of 30)

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- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels](https://mlsanity.com/paper/2312.15972/) (2023)
- [Self-Supervised Learning for Image Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2505.13584/) (2025)
- [A Novel Collaborative Self-Supervised Learning Method for Radiomic Data](https://mlsanity.com/paper/2302.09807/) (2023)
- [Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI](https://mlsanity.com/paper/2410.00410/) (2024)
- [2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks](https://mlsanity.com/paper/2103.05259/) (2021)
- [Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning](https://mlsanity.com/paper/2211.08559/) (2022)
- [Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis](https://mlsanity.com/paper/2306.05297/) (2023)
- [Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis](https://mlsanity.com/paper/2106.12313/) (2021)
- [An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation](https://mlsanity.com/paper/2109.00622/) (2021)
- [Using the Order of Tomographic Slices as a Prior for Neural Networks Pre-Training](https://mlsanity.com/paper/2203.09372/) (2022)
- [CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training](https://mlsanity.com/paper/2601.12282/) (2026)
- [Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines](https://mlsanity.com/paper/2605.23995/) (2026)

## Abstract (arXiv)

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.