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.

翻译：前列腺癌（PCa）对男性健康构成重大威胁，早期诊断对于改善预后和降低死亡率至关重要。经直肠超声（TRUS）在前列腺癌的诊断和图像引导介入中发挥着关键作用。为辅助医生进行更精准高效的计算机辅助诊断与介入，大量TRUS图像处理算法被提出，并在多项任务中取得了先进性能，包括前列腺腺体分割、前列腺图像配准、PCa分类与检测以及介入针检测。过去二十年间这些算法的快速发展亟需全面总结。为此，本文对该领域进行了叙述性分析，梳理了TRUS图像分析背景下图像处理方法的演进脉络，并着重阐明了其相关贡献。此外，本文还探讨了当前面临的挑战，并提出了未来可能推动该领域进一步发展的研究方向。