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A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements

机译:一个机电平台用于通过刚度和超声测量在病理解剖学分析中计算机辅助检测结节

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摘要

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanical and acoustical properties of tissues, because of changes in stiffness and density. A force sensor and an ultrasound probe were combined to detect such alterations during force-regulated indentations. To explore the specimens, regardless of their orientation and shape, a scanned area of the test sample was defined using shape recognition applying optical background subtraction to the images captured by a camera. The motorized platform was validated using seven phantom tissues, simulating the mechanical and acoustical properties of ex-vivo diseased tissues, including stiffer nodules that can be encountered in pathological conditions during histological analyses. Results demonstrated the platform’s ability to automatically explore and identify the inclusions in the phantom. Overall, the system was able to correctly identify up to 90.3% of the inclusions by means of stiffness in combination with ultrasound measurements, paving pathways towards robotic palpation during intraoperative examinations.
机译:这项研究为癌症结节的离体检测提供了一个平台,解决了手术中医学诊断的自动化以及相关的组织学分析。由于刚度和密度的变化,所提出的方法利用癌症的性质来改变组织的机械和声学性质。力传感器和超声探头结合在一起以检测力调节压痕期间的这种变化。为了探索样品,无论它们的方向和形状如何,都使用形状识别来定义测试样品的扫描区域,将光学背景减法应用于相机捕获的图像。电动平台已使用七个幻像组织进行了验证,模拟了离体病变组织的机械和声学特性,包括组织学分析在病理条件下可能遇到的较硬的结节。结果表明,该平台具有自动探索和识别幻像中包含物的能力。总体而言,该系统能够通过硬度结合超声测量正确识别多达90.3%的夹杂物,为术中检查过程中机器人触诊铺平了道路。

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