Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, delivering comparable accuracy for diagnosing prostate cancer to MRI but at a lower cost. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. This paper proposes a deep learning approach for automated, fast, and accurate prostate segmentation on micro-US images. Prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. We introduce MicroSegNet, a multi-scale annotation-guided Transformer UNet model to address this challenge. During the training process, MicroSegNet focuses more on regions that are hard to segment (challenging regions), where expert and non-expert annotations show discrepancies. We achieve this by proposing an annotation-guided cross entropy loss that assigns larger weight to pixels in hard regions and lower weight to pixels in easy regions. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.942 and a Hausdorff distance of 2.11 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. We will make our code and dataset publicly available to promote transparency and collaboration in research.

翻译：显微超声（micro-US）是一种新型29MHz超声技术，其分辨率较传统超声提高3-4倍，在诊断前列腺癌方面可提供与MRI相当的准确性，且成本更低。准确的前列腺分割对于前列腺体积测量、癌症诊断、前列腺活检及治疗规划至关重要。本文提出一种基于深度学习的自动、快速且精确的前列腺显微超声图像分割方法。由于显微超声图像中存在伪影，且前列腺、膀胱及尿道中线区域边界模糊，使得前列腺分割具有挑战性。我们引入MicroSegNet——一种多尺度标注引导的Transformer UNet模型来解决该问题。在训练过程中，MicroSegNet更关注难以分割的区域（挑战性区域），即专家与非专家标注存在差异的区域。通过提出一种标注引导的交叉熵损失函数实现此目标，该函数对困难区域的像素赋予较大权重，而对简单区域像素赋予较小权重。我们使用55名患者的显微超声图像训练模型，并在20名患者数据上进行评估。MicroSegNet模型的Dice系数达到0.942，豪斯多夫距离为2.11 mm，性能优于多种最先进的分割方法及三名不同经验水平的人工标注者。我们将公开发布代码和数据集，以促进研究透明性与协作。