Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. 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 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://github.com/mirthAI/MicroSegNet and our dataset is publicly available at https://zenodo.org/records/10475293.

翻译：微型超声（Micro-US）是一种新型29 MHz超声技术，其分辨率比传统超声高3-4倍，有望实现低成本、高准确性的前列腺癌诊断。精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。然而，由于中线处前列腺、膀胱和尿道之间的伪影及边界模糊，微型超声下的前列腺分割极具挑战性。本文提出MicroSegNet——一种专为应对这些挑战而设计的多尺度标注引导Transformer UNet模型。在训练过程中，MicroSegNet更聚焦于难分割区域（硬区域），这些区域以专家与非专家标注之间的差异为特征。我们通过提出一种标注引导的二元交叉熵（AG-BCE）损失函数实现这一目标，该函数对硬区域的预测误差赋予更大权重，而对易区域的预测误差赋予更小权重。通过利用多尺度深度监督，AG-BCE损失函数被无缝集成到训练过程中，使MicroSegNet能够捕获不同尺度的全局上下文依赖关系与局部信息。我们使用55名患者的微型超声图像训练模型，随后在20名患者上进行评估。MicroSegNet模型的Dice系数达到0.939，豪斯多夫距离为2.02 mm，不仅优于多种最先进的分割方法，也优于三位不同经验水平的人工标注者。我们的代码已在https://github.com/mirthAI/MicroSegNet公开，数据集已在https://zenodo.org/records/10475293公开。