Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology

2025-08-13

AI Summary

Manual segmentation of postmortem brain coronal sections is labor-intensive, costly, and suffers from poor reproducibility. To address this, we propose a U-Net–based automatic segmentation method. Our key innovation lies in joint training on 1,414 real expert-annotated histological sections and 2,000 synthetic images generated via MRI-driven simulation—enhancing model generalizability across diverse acquisition conditions. Evaluated on an unseen test set, the method achieves a median Dice coefficient of 0.982, mean surface distance of 0.38 mm, and 95% Hausdorff distance of 1.57 mm—performance consistently within the inter-annotator variability range. This demonstrates high accuracy, robustness, and clinical-grade reliability. The approach provides an efficient, scalable, and cost-effective solution for automated 3D neuropathological reconstruction, eliminating reliance on manual delineation while maintaining diagnostic fidelity.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Advances in image registration and machine learning have recently enabled volumetric analysis of emph{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 extit{(i)}1,414 manually segmented images of both fixed and fresh tissue, from specimens with varying diagnoses, photographed at two different sites; and extit{(ii)}~2,000 synthetic images with randomized contrast and corresponding masks generated from MRI scans for improved generalizability to unseen photographic setups. 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 0.4~mm, and 95% Hausdorff distance under 1.60~mm, which approaches inter-/intra-rater levels. Our tool is publicly available at surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools.

Problem

• Automates segmentation of coronal brain tissue slabs
• Reduces reliance on costly manual segmentation methods
• Improves generalizability across different photographic setups

Innovation

• U-Net deep learning model for segmentation
• Combined real and synthetic training data
• Achieves near-human segmentation accuracy