BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis.

As neurodegenerative disease pathological hallmarks have been reported in both grey matter (GM) and white matter (WM) with different density distributions, automating the segmentation process of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing a scalable analysis on pathology images. This paper proposes an automated segmentation pipeline (BrainSec) combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues. The final output generates XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, BrainSec, to classify the GM/WM/background regions. We demonstrate BrainSec is robust and has reliable performance by testing it on over 180 WSIs that incorporate numerous unique cases as well as distinct neuroanatomic brain regions. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights. In addition, we have integrated BrainSec with an existing Amyloid-ß pathology classification model into a unified framework (without incurring significant computation complexity) to identify pathologies, visualize their distributions, and quantify each type of pathologies in segmented GM/WM regions, respectively.

A Semi-supervised Learning for Segmentation of Gigapixel Histopathology Images from Brain Tissues.

Automated segmentation of grey matter (GM) and white matter (WM) in gigapixel histopathology images is advantageous to analyzing distributions of disease pathologies, further aiding in neuropathologic deep phenotyping. Although supervised deep learning methods have shown good performance, its requirement of a large amount of labeled data may not be cost-effective for large scale projects. In the case of GM/WM segmentation, trained experts need to carefully trace the delineation in gigapixel images. To minimize manual labeling, we consider semi-surprised learning (SSL) and deploy one state-of-the-art SSL method (FixMatch) on WSIs. Then we propose a two-stage scheme to further improve the performance of SSL: the first stage is a self-supervised module to train an encoder to learn the visual representations of unlabeled data, subsequently, this well-trained encoder will be an initialization of consistency loss-based SSL in the second stage. We test our method on Amyloid-ß stained histopathology images and the results outperform FixMatch with the mean IoU score at around 2% by using 6,000 labeled tiles while over 10% by using only 600 labeled tiles from 2 WSIs.Clinical relevance- this work minimizes the required labeling efforts by trained personnel. An improved GM/WM segmentation method could further aid in the study of brain diseases, such as Alzheimer's disease.