Weekly supervised convolutional long short-term memory neural networks for MR-TRUS registration

Qiulan Zeng; Yabo Fu; Jiwoong Jeong; Tian Zhen; Tonghe Wang; Yang Lei; Hui Mao; Ashesh B. Jani; Pretesh Patel; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2549760

16 March 2020 Weekly supervised convolutional long short-term memory neural networks for MR-TRUS registration

Qiulan Zeng, Yabo Fu, Jiwoong Jeong, Tian Zhen, Tonghe Wang, Yang Lei, Hui Mao, Ashesh B. Jani, Pretesh Patel, Walter J. Curran, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography; 1131910 (2020) https://doi.org/10.1117/12.2549760
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

We propose an approach based on a weekly supervised method for MR-TRUS image registration. Inspired by the viscous fluid physical model, we made the first attempt at combining convolutional neural network (CNN) and long short-term memory (LSTM) Neural Network to perform deep learning-based dense deformation field prediction. Through the integration of convolutional long short-term memory (ConvLSTM) Neural Network and weakly supervised approach, we achieved accurate results in terms of Dice similarity coefficient (DSC) and target registration error (TRE) without using conventional intensity-based image similarity measures. Thirty-six sets of patient data were used in the study. Experimental results showed that our proposed ConvLSTM neural network produced a mean TRE of 2.85±1.72 mm and a mean Dice of 0.89.

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Qiulan Zeng, Yabo Fu, Jiwoong Jeong, Tian Zhen, Tonghe Wang, Yang Lei, Hui Mao, Ashesh B. Jani, Pretesh Patel, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Weekly supervised convolutional long short-term memory neural networks for MR-TRUS registration", Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 1131910 (16 March 2020); https://doi.org/10.1117/12.2549760

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