# Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

## TL;DR

This paper introduces a generative probabilistic model using anatomical priors in convolutional neural networks to enable fast, unsupervised biomedical image segmentation without paired training data, demonstrated on brain MRI datasets.

## Contribution

It presents a novel approach that incorporates anatomical priors into CNNs for unsupervised segmentation, applicable across different datasets and imaging modalities.

## Key findings

- Enables fast unsupervised segmentation with anatomical priors.
- Effective on large multi-study brain MRI dataset.
- Facilitates segmentation in data-scarce clinical scenarios.

## Abstract

We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior can enable fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code is freely available at http://github.com/adalca/neuron.