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Domain Specific Transporter Framework to Detect Fractures in Ultrasound
arXiv - CS - Machine Learning Pub Date : 2021-06-09 , DOI: arxiv-2106.05929
Arpan Tripathi, Abhilash Rakkunedeth, Mahesh Raveendranatha Panicker, Jack Zhang, Naveenjyote Boora, Jacob Jaremko

Ultrasound examination for detecting fractures is ideally suited for Emergency Departments (ED) as it is relatively fast, safe (from ionizing radiation), has dynamic imaging capability and is easily portable. High interobserver variability in manual assessment of ultrasound scans has piqued research interest in automatic assessment techniques using Deep Learning (DL). Most DL techniques are supervised and are trained on large numbers of labeled data which is expensive and requires many hours of careful annotation by experts. In this paper, we propose an unsupervised, domain specific transporter framework to identify relevant keypoints from wrist ultrasound scans. Our framework provides a concise geometric representation highlighting regions with high structural variation in a 3D ultrasound (3DUS) sequence. We also incorporate domain specific information represented by instantaneous local phase (LP) which detects bone features from 3DUS. We validate the technique on 3DUS videos obtained from 30 subjects. Each ultrasound scan was independently assessed by three readers to identify fractures along with the corresponding x-ray. Saliency of keypoints detected in the image\ are compared against manual assessment based on distance from relevant features.The transporter neural network was able to accurately detect 180 out of 250 bone regions sampled from wrist ultrasound videos. We expect this technique to increase the applicability of ultrasound in fracture detection.

中文翻译:

用于检测超声骨折的特定领域转运蛋白框架

用于检测骨折的超声检查非常适合急诊科 (ED),因为它相对快速、安全(免受电离辐射)、具有动态成像能力且易于携带。超声扫描手动评估中观察者间的高变异性激发了对使用深度学习 (DL) 的自动评估技术的研究兴趣。大多数深度学习技术都受到监督,并在大量标记数据上进行训练,这些数据非常昂贵,并且需要专家进行数小时的仔细注释。在本文中,我们提出了一种无监督的、特定领域的转运框架,用于从腕部超声扫描中识别相关关键点。我们的框架提供了简洁的几何表示,突出显示了 3D 超声 (3DUS) 序列中具有高度结构变化的区域。我们还结合了由瞬时局部相位 (LP) 表示的域特定信息,它从 3DUS 中检测骨骼特征。我们在从 30 个受试者获得的 3DUS 视频上验证了该技术。每次超声扫描均由三位阅读器独立评估,以识别骨折以及相应的 X 射线。将图像中检测到的关键点的显着性与基于与相关特征的距离的人工评估进行比较。转运神经网络能够准确检测从手腕超声视频采样的 250 个骨骼区域中的 180 个。我们希望这种技术能够提高超声波在骨折检测中的适用性。每次超声扫描均由三位阅读器独立评估,以识别骨折以及相应的 X 射线。将图像中检测到的关键点的显着性与基于与相关特征的距离的人工评估进行比较。转运神经网络能够准确检测从手腕超声视频采样的 250 个骨骼区域中的 180 个。我们希望这种技术能够提高超声波在骨折检测中的适用性。每次超声扫描均由三位阅读器独立评估,以识别骨折以及相应的 X 射线。将图像中检测到的关键点的显着性与基于与相关特征的距离的人工评估进行比较。转运神经网络能够准确检测从手腕超声视频采样的 250 个骨骼区域中的 180 个。我们希望这种技术能够提高超声波在骨折检测中的适用性。
更新日期:2021-06-11
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