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Proceedings Paper

Deformable alignment of longitudinal postoperative brain GBM scans using deep learning
Author(s): Yi Lao; Victoria Yu; Eric Chang; Wensha Yang; Ke Sheng
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Paper Abstract

Longitudinal brain alignment is critical for disease monitoring and adaptive treatment planning in glioblastoma (GBM) patients. However, the current methods are either non-adaptive to pathological brains, or time and laborintensive. Here, we aim to develop a novel deep-learning-based framework for longitudinal postoperative brain GBM scan registration. The proposed pathology adaptive registration framework (PARF) adopts a double UNET architecture: a 2D 7-level UNET, NETseg, for pathology segmentation, and a 3D 5-level UNET, NETreg, for unsupervised image registration, connected through a spatial transformer and a volume combiner. NETseg was first trained separately and then combined with NETreg for pathology adaptive registration training. In aggregated registration testing of PARF, 36 registrations from 18 intra-subject pairs of post-operative follow-up MR scans were selected, and the results were compared to those from current state-of-the-art methods as well as non-adaptive NETreg alone. PARF is significantly faster and more accurate than comparison methods, in terms of sum-of-squared differences, segmentation alignment dice coefficients, and landmark mislignment errors. PARF may pave the path for various clinical and research applications that depend on the accurate registration of GBM longitudinal images.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130O (10 March 2020); doi: 10.1117/12.2549129
Show Author Affiliations
Yi Lao, Univ. of California-Los Angeles (United States)
Victoria Yu, Univ. of California-Los Angeles (United States)
Eric Chang, Keck School of Medicine, Univ. of Southern California (United States)
Wensha Yang, Keck School of Medicine, Univ. of Southern California (United States)
Ke Sheng, Univ. of California-Los Angeles (United States)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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