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Multitask Weakly Supervised Generative Network for MR-US Registration

计算机科学 人工智能 图像配准 试验装置 豪斯多夫距离 磁共振成像 超声波 计算机视觉 深度学习 基本事实 生成模型 模式识别(心理学) 生成语法 图像(数学) 放射科 医学
作者
Mohammad Farid Azampour,Kristina Mach,Emad Fatemizadeh,Beatrice Demiray,Kay Markus Westenfelder,Katja Steiger,Matthias Eiber,Thomas Wendler,Bernhard Kainz,Nassir Navab
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:: 1-1 被引量:1
标识
DOI:10.1109/tmi.2024.3400899
摘要

Registering pre-operative modalities, such as magnetic resonance imaging or computed tomography, to ultrasound images is crucial for guiding clinicians during surgeries and biopsies. Recently, deep-learning approaches have been proposed to increase the speed and accuracy of this registration problem. However, all of these approaches need expensive supervision from the ultrasound domain. In this work, we propose a multitask generative framework that needs weak supervision only from the pre-operative imaging domain during training. To perform a deformable registration, the proposed framework translates a magnetic resonance image to the ultrasound domain while preserving the structural content. To demonstrate the efficacy of the proposed method, we tackle the registration problem of pre-operative 3D MR to transrectal ultrasonography images as necessary for targeted prostate biopsies. We use an in-house dataset of 600 patients, divided into 540 for training, 30 for validation, and the remaining for testing. An expert manually segmented the prostate in both modalities for validation and test sets to assess the performance of our framework. The proposed framework achieves a 3.58 mm target registration error on the expert-selected landmarks, 89.2% in the Dice score, and 1.81 mm 95th percentile Hausdorff distance on the prostate masks in the test set. Our experiments demonstrate that the proposed generative model successfully translates magnetic resonance images into the ultrasound domain. The translated image contains the structural content and fine details due to an ultrasound-specific two-path design of the generative model. The proposed framework enables training learning-based registration methods while only weak supervision from the pre-operative domain is available.
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