Multi-parametric Magnetic Resonance Imaging Fusion for Automatic Classification of Prostate Cancer

Huang, Weikai1; Wang, Xiangyu2; Huang, Yijin3; Lin, Fan2; Tang, Xiaoying1

10.1109/EMBC48229.2022.9871334

2022

44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (IEEE EMBC)

2375-7477

978-1-7281-2783-5

44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (IEEE EMBC)

471-474

11-15 July 2022

Glasgow, Scotland, United Kingdom

Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mp-MRI) has recently gained great research interest. In this work, a fully automatic CAD pipeline of PCa using mp-MRI data is presented. In order to fully explore the mp-MRI data, we systematically investigate three multi-modal medical image fusion strategies in convolutional neural networks, namely input-level fusion, feature-level fusion, and decision-level fusion. Extensive experiments are conducted on two datasets with different PCa-related diagnostic tasks. We identify a pipeline that works relatively the best for both diagnostic tasks, two important components of which are stacking three adjacent slices as the input and performing decision-level fusion with specific loss weights. Clinical relevance- This work provides a practical method for automated diagnosis of PCa based on multi-parametric MRI

Training Solid Modeling Magnetic Resonance Imaging Pipelines Stacking Feature Extraction Lesions

第一 ; 通讯

CPCI-S ; EI

IEEE

1.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
2.Department of Radiology, Shenzhen Second People's Hospital, Shenzhen, China
3.School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada

Huang, Weikai,Wang, Xiangyu,Huang, Yijin,et al. Multi-parametric Magnetic Resonance Imaging Fusion for Automatic Classification of Prostate Cancer[C],2022:471-474.