Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images

• URL: http://arxiv.org/abs/2502.00712v1
• Date: Sun, 02 Feb 2025 07:58:40 GMT
• Title: Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images
• Authors: Shengtian Sang, Hassan Jahanandish, Cynthia Xinran Li, Indrani Bhattachary, Jeong Hoon Lee, Lichun Zhang, Sulaiman Vesal, Pejman Ghanouni, Richard Fan, Geoffrey A. Sonn, Mirabela Rusu,
• Abstract summary: We propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations.<n>Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132)<n>This framework demonstrates the potential for reducing the complexity of prostate cancer diagnosis and provides a flexible architecture applicable to other multimodal medical imaging tasks.
• Score: 2.5096595271293185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by combining MRI's detailed visualization with TRUS's real-time guidance, it is a complex and time-intensive procedure that relies heavily on manual annotations, leading to potential errors. To address these challenges, we propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations. Unlike traditional multimodal fusion approaches that rely on naive data concatenation, our method integrates a registration-segmentation framework to align and leverage spatial information between MRI and TRUS modalities. This alignment enhances segmentation accuracy and reduces reliance on manual effort. Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132) methods, with significant improvements (p $<$ 0.01). This framework demonstrates the potential for reducing the complexity of prostate cancer diagnosis and provides a flexible architecture applicable to other multimodal medical imaging tasks.

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