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.

本文提出了一种基于深度学习的方法，即 MicroSegNet，用于对微型超声图像上的前列腺进行自动化、快速、准确的分割，使用了多尺度注释引导变压器 UNET 模型，可以更好地处理前列腺、膀胱和尿道的艺术品和不清晰的图像区域，取得了比现有分割方法更好的结果，还提供了代码与数据集。

MicroSegNet: 微超声图像前列腺分割的深度学习方法

MicroSegNet: A Deep Learning Approach for Prostate Segmentation on  Micro-Ultrasound Images

Early diagnosis of prostate cancer significantly improves a patient's 5-year
survival rate. Biopsy of small prostate cancers is improved with image-guided
biopsy. MRI-ultrasound fusion-guided biopsy is sensitive to smaller tumors but
is underutilized due to the high cost of MRI and fusion equipment.
Micro-ultrasound (micro-US), a novel high-resolution ultrasound technology,
provides a cost-effective alternative to MRI while delivering comparable
diagnostic accuracy. However, the interpretation of micro-US is challenging due
to subtle gray scale changes indicating cancer vs normal tissue. This challenge
can be addressed by training urologists with a large dataset of micro-US images
containing the ground truth cancer outlines. Such a dataset can be mapped from
surgical specimens (histopathology) onto micro-US images via image
registration. In this paper, we present a semi-automated pipeline for
registering in vivo micro-US images with ex vivo whole-mount histopathology
images. Our pipeline begins with the reconstruction of pseudo-whole-mount
histopathology images and a 3D micro-US volume. Each pseudo-whole-mount
histopathology image is then registered with the corresponding axial micro-US
slice using a two-stage approach that estimates an affine transformation
followed by a deformable transformation. We evaluated our registration pipeline
using micro-US and histopathology images from 18 patients who underwent radical
prostatectomy. The results showed a Dice coefficient of 0.94 and a landmark
error of 2.7 mm, indicating the accuracy of our registration pipeline. This
proof-of-concept study demonstrates the feasibility of accurately aligning
micro-US and histopathology images. To promote transparency and collaboration
in research, we will make our code and dataset publicly available.

使用图像注册技术，作者提出了对微型超声进一步应用的可能性，利用大型数据集辅助训练医生进行识别并达到较高的准确率，有望应用于前列腺癌的早期诊断和治疗中。