AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

TL;DR

This review surveys AI-based automatic prostate segmentation methods across multi-modality images, discussing their advantages, limitations, evaluation metrics, challenges, and future research directions to improve prostate cancer detection and treatment.

Contribution

It provides a comprehensive classification and evaluation of existing AI-driven prostate segmentation techniques and discusses future development trends in the field.

Findings

Survey of various AI segmentation methods and their pros and cons.

Introduction of new evaluation metrics for segmentation performance.

Discussion of current challenges and future research directions.

Abstract

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.