AI-based Automatic Prostate Gland Segmentation in Multi-modal Medical Imaging: A Review

Prostate cancer poses a significant threat to health, necessitating early detection to reduce mortality rates among patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The rise of precision medicine and increased clinical demand have driven the need for data-driven tasks in medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This paper introduces a novel classification method that differentiates supervision types based on their quantity and nature during training. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, evaluating their respective strengths and limitations. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue.

Duke Scholars

Author Jing Cai Radiation Oncology