Prof. Eric Chang Profile

Prof. Eric Chang

at Univ of Southern California

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Proceedings Article | 10 March 2020 Presentation + Paper

Deformable alignment of longitudinal postoperative brain GBM scans using deep learning

Yi Lao, Victoria Yu, Eric Chang, Wensha Yang, Ke Sheng

Proceedings Volume 11313, 113130O (2020) https://doi.org/10.1117/12.2549129

KEYWORDS: Image registration, Brain, Image segmentation, Neuroimaging, Pathology, Transformers, Network architectures, Tissues, 3D image processing, Radiation oncology

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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, NET_seg, for pathology segmentation, and a 3D 5-level UNET, NET_reg, for unsupervised image registration, connected through a spatial transformer and a volume combiner. NET_seg was first trained separately and then combined with NET_reg 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 NET_reg 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.

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