Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology

TL;DR

This paper presents a deep learning-based automated segmentation tool for coronal brain tissue slabs, significantly reducing manual effort and achieving near-human accuracy in volumetric brain tissue analysis from photographs.

Contribution

The study introduces a U-Net based model trained on a large, diverse dataset to automate tissue segmentation in postmortem brain images, improving efficiency and consistency.

Findings

Median Dice score over 0.98 indicating high accuracy

Surface distance under 0.4mm demonstrating precise segmentation

Approaches inter-/intra-rater variability levels

Abstract

Advances in image registration and machine learning have recently enabled volumetric analysis of postmortem brain tissue from conventional photographs of coronal slabs, which are routinely collected in brain banks and neuropathology laboratories worldwide. One caveat of this methodology is the requirement of segmentation of the tissue from photographs, which currently requires costly manual intervention. In this article, we present a deep learning model to automate this process. The automatic segmentation tool relies on a U-Net architecture that was trained with a combination of 1,414 manually segmented images of both fixed and fresh tissue, from specimens with varying diagnoses, photographed at two different sites. Automated model predictions on a subset of photographs not seen in training were analyzed to estimate performance compared to manual labels, including both inter- and intra-rater variability. Our model achieved a median Dice score over 0.98, mean surface distance under 0.4mm, and 95\% Hausdorff distance under 1.60mm, which approaches inter-/intra-rater levels. Our tool is publicly available at surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools.