Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision

• URL: http://arxiv.org/abs/2211.07876v1
• Date: Tue, 15 Nov 2022 03:58:47 GMT
• Title: Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision
• Authors: Mingyuan Meng, Lei Bi, Dagan Feng, and Jinman Kim
• Abstract summary: We focus on brain tumor sequence registration between pre-operative and follow-up MRI scans of brain glioma patients. Brain tumor registration is a fundamental requirement in brain image analysis for quantifying tumor changes. We adopt our recently proposed Non-Iterative Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method for coarse-to-fine registering images with large deformations.
• Score: 11.795108660250843
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022). Brain tumor registration is a fundamental requirement in brain image analysis for quantifying tumor changes. This is a challenging task due to large deformations and missing correspondences between pre-operative and follow-up scans. For this task, we adopt our recently proposed Non-Iterative Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method for coarse-to-fine registering images with large deformations. To overcome missing correspondences, we extend the NICE-Net by introducing dual deep supervision, where a deep self-supervised loss based on image similarity and a deep weakly-supervised loss based on manually annotated landmarks are deeply embedded into the NICE-Net. At the BraTS-Reg 2022, our method achieved a competitive result on the validation set (mean absolute error: 3.387) and placed 4th in the final testing phase (Score: 0.3544).

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