A deep learning pipeline for automatic skull stripping and brain segmentation

Chandan Ganesh Bangalore Yogananda, Benjamin C. Wagner, Gowtham K. Murugesan, Ananth Madhuranthakam, Joseph A. Maldjian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

A deep learning pipeline (DLP) with a triple network framework was developed to perform skull stripping and segment the brain into gray matter, white matter and cerebrospinal fluid (CSF) using T1w Magnetic Resonance (MR) images. Three separate 3D Dense-Unets were designed to decompose the complex skull stripping and brain segmentation problems into individual binary segmentation problems to segment a particular label using a 32x32x32 patch based approach. These included a skull stripping network to obtain the brain mask (BM), GW-net to segment gray matter (GM) and white matter (WM), and CSF-net to segment cerebrospinal fluid (CSF). The networks consisted of seven dense blocks with each block containing four layers. Every layer was connected to every other layer in that dense block. Each layer consisted of four sublayers namely, BatchNormalization, 3D Convolution, ReLu and dropout. As a part of the iTAKL study [1], 785 T1w MR datasets including 288 high school (14-18 years) and 497 youth (9-13 years) datasets were used. On the evaluation dataset of 50 held-out subjects, dice scores of (a) 0.980, 0.92, 0.94 and 0.845 for BM, GM, WM and CSF respectively on down sampled data, and (b) 0.983, 0.9103, 0.9277 and 0.83 for BM, GM, WM and CSF respectively on the full resolution data were achieved. The pipeline was then tested on datasets from the AADHS study and 5 other studies from the Human Connectome project (HCP) [2, 3] with comparable performance.

Original languageEnglish (US)
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages727-731
Number of pages5
ISBN (Electronic)9781538636411
DOIs
StatePublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
CountryItaly
CityVenice
Period4/8/194/11/19

Keywords

  • BM
  • CSF
  • Dense-unet
  • DLP
  • GM
  • MRI
  • WM

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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