Joint bleeding is a common condition for people with hemophilia and, if untreated, can result in hemophilic arthropathy. Ultrasound imaging has recently emerged as an effective tool to diagnose joint recess distension caused by joint bleeding. However, no computer-aided diagnosis tool exists to support the practitioner in the diagnosis process. This paper addresses the problem of automatically detecting the recess and assessing whether it is distended in knee ultrasound images collected in patients with hemophilia. After framing the problem, we propose two different approaches: the first one adopts a one-stage object detection algorithm, while the second one is a multi-task approach with a classification and a detection branch. The experimental evaluation, conducted with $483$ annotated images, shows that the solution based on object detection alone has a balanced accuracy score of $0.74$ with a mean IoU value of $0.66$, while the multi-task approach has a higher balanced accuracy value ($0.78$) at the cost of a slightly lower mean IoU value.
翻译:联合出血是血友病患者的常见情况,如果未经治疗,可导致血友病动脉病。超声波成像最近成为诊断因共同出血引起的联合休眠失常的有效工具;然而,没有计算机辅助诊断工具支持执业者诊断过程。本文涉及自动检测休眠和评估在血友病患者所收集的膝盖超声图像中是否分泌的问题。在提出问题后,我们提出两种不同办法:第一个办法是采用单级物体检测算法,第二个是分类和检测分支的多功能方法。以483美元附加图象进行的实验性评价显示,单凭物体检测得出的解决方案的精度为0.74美元,平均IoU值为0.66美元,而多塔斯克方法的精度值较高(0.78美元),其成本是略微低的IoU值。