BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis

Zhengfeng Lai, Luca Cerny Oliveira, Runlin Guo, Wenda Xu, Zin Hu, Kelsey Mifflin, Charles Decarli, Sen Ching Cheung, Chen Nee Chuah, Brittany N. Dugger

Research output: Contribution to journalArticlepeer-review

Abstract

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-β 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.

Original languageEnglish
Pages (from-to)49064-49079
Number of pages16
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation (NSF) HDR: TRIPODS under Grant CCF-1934568, in part by the National Institute on Aging of the National Institutes of Health under Award P30AG010129, P30AG072972, and R01AG062517, in part by the University of California Office of the President (MRI-19-599956), in part by the California Department of Public Health Alzheimer's Disease Program (Grant 19-10611), and in part by the 2019 California Budget Act.

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Alzheimer's disease
  • Convolutional neural network
  • Dementia
  • Machine learning
  • Medical image analysis
  • Neuropathology

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)
  • Electrical and Electronic Engineering

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