Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation

• URL: http://arxiv.org/abs/2303.14998v1
• Date: Mon, 27 Mar 2023 08:42:10 GMT
• Title: Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation
• Authors: Bogyeong Kang, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo, and Tae-Eui Kam
• Abstract summary: We propose a multi-view image translation framework, which can translate contrast-enhanced T1 (ceT1) MR imaging to high-resolution T2 (hrT2) MR imaging. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive constraint, respectively. Thereby, we can augment pseudo-hrT2 images reflecting different perspectives, which eventually lead to a high-performing segmentation model.
• Score: 0.7829352305480285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a multi-view image translation framework, which can translate contrast-enhanced T1 (ceT1) MR imaging to high-resolution T2 (hrT2) MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive constraint, respectively. Thereby, we can augment pseudo-hrT2 images reflecting different perspectives, which eventually lead to a high-performing segmentation model. Our experimental results on the CrossMoDA challenge show that the proposed method achieved enhanced performance on the vestibular schwannoma and cochlea segmentation.

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