A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound

TL;DR

This paper reviews the evolution of image processing techniques in transrectal ultrasound for prostate cancer diagnosis, highlighting advancements, current challenges, and future research directions to improve accuracy and efficiency.

Contribution

It provides a comprehensive narrative summary of two decades of image processing algorithms in TRUS for prostate cancer, emphasizing their development and impact.

Findings

Significant progress in prostate gland segmentation and detection algorithms.

Current challenges include image quality and algorithm robustness.

Future directions involve deep learning and multimodal data integration.

Abstract

Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) plays a vital role in the diagnosis and image-guided intervention of PCa.To facilitate physicians with more accurate and efficient computer-assisted diagnosis and interventions, many image processing algorithms in TRUS have been proposed and achieved state-of-the-art performance in several tasks, including prostate gland segmentation, prostate image registration, PCa classification and detection, and interventional needle detection. The rapid development of these algorithms over the past two decades necessitates a comprehensive summary. In consequence, this survey provides a \textcolor{blue}{narrative } analysis of this field, outlining the evolution of image processing methods in the context of TRUS image analysis and meanwhile highlighting their relevant contributions. Furthermore, this survey discusses current challenges and suggests future research directions to possibly advance this field further.