  Prostate cancer represents a major threat to health. Early detection is vital
in reducing the mortality rate among prostate cancer 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 advent of precision medicine and a significant increase
in clinical capacity have spurred the need for various data-driven tasks in the
field of medical imaging. Recently, numerous machine learning and data mining
tools have been integrated into various medical areas, including image
segmentation. This article proposes a new classification method that
differentiates supervision types, either in number or kind, during the training
phase. Subsequently, we conducted a survey on artificial intelligence
(AI)-based automatic prostate segmentation methods, examining the advantages
and limitations of each. 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.
