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2 April 2024 Multistream fusion segmentation and classification of prostate lesions from magnetic resonance images
Rongfeng Wei, Wenxu Zhang, Weixuan Kou, Cristian Rey, Harry Marshall, Bernard Chiu
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Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292629 (2024) https://doi.org/10.1117/12.3006245
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
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Abstract
Prostate cancer is a significant contributor to cancer-related deaths in men. Detecting prostate cancer early can greatly increase the likelihood of successful treatment. However, detecting and assessing prostate lesions from multiparametric magnetic resonance images (MRI) is time-consuming and variable across radiologists with different levels of experience. We present an integrated framework for segmenting and classifying prostate lesions from MRI. The proposed approach is in contrast with most existing automated prostate analysis approaches, which treat segmentation and classification of prostate lesions as two separate tasks with no interactions between them. In the proposed framework, preliminary lesion boundaries were first segmented from T2-weighted (T2W) and diffusion-weighted images (DWI) by a three-stream network. The region of interest (ROI) enclosing the segmented lesion was fed to a weakly supervised classification network, which predicted the Gleason grade of the lesion and provided the class activation maps (CAMs) corresponding to multiple MRI modalities. Finally, MR images of different modalities with the corresponding CAMs were fed to a six-stream network to generate an enhanced lesion mask. Our experiments showed that CAMs generated by the proposed weakly supervised classifier improved segmentation performance. Our proposed method has a great potential to improve the accuracy and efficiency of prostate MRI interpretation workflow.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Rongfeng Wei, Wenxu Zhang, Weixuan Kou, Cristian Rey, Harry Marshall, and Bernard Chiu "Multistream fusion segmentation and classification of prostate lesions from magnetic resonance images", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292629 (2 April 2024); https://doi.org/10.1117/12.3006245
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KEYWORDS
Image segmentation
Prostate
Image classification
Image fusion
Magnetic resonance imaging
Prostate cancer
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