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Instance-Based Segmentation for Boundary Detection of Neuropathic Ulcers Through Mask-RCNN

机译：基于实例的分割，通过mask-RCNN进行神经性溃疡的边界检测

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摘要

Neuropathic ulcers form and proliferate because of peripheral neuropathy, usually in diabetic patients. The existing ulcer assessment process which relies on visual examination, potentially be imprecise and inefficient. Therefore this indicates the necessity of a more quantitative and cost-effective solution that enables ulcer diagnosing process much faster. In the current literature, different deep learning approaches are available for diagnosing illnesses through medical imagery. When diagnosing diabetic patients who are suffering from neuropathic ulcers through imagery, the locating and segmenting of ulcer boundaries is of great importance. In this study, we propose an approach to automate the process of locating and segmenting ulcers through Mask-RCNN model. We use a dataset of 400 ulcer imagery and corresponding annotations of ulcers for this task. This approach achieves an overall ulcer detection average precision (AP) at Intersection over union (IoU) threshold 0.5 of 0.8632 and mean average precision (mAP) at Intersection over union (IoU) threshold 0.5 to 0.95 by steps of size 0.05 of 0.5084 for ResNet-101 backbone.
机译：通常在糖尿病患者中，由于周围神经病变而导致神经性溃疡的形成和扩散。现有的依靠视觉检查的溃疡评估过程可能不精确且效率低下。因此，这表明需要更定量和更具成本效益的解决方案，以更快地诊断溃疡。在当前的文献中，可通过医学影像来诊断疾病的方法有多种不同的深度学习方法。当通过影像诊断患有神经性溃疡的糖尿病患者时，溃疡边界的定位和分割非常重要。在这项研究中，我们提出了一种通过Mask-RCNN模型自动定位和分割溃疡过程的方法。我们为此任务使用了400个溃疡图像的数据集和相应的溃疡注释。对于ResNet，此方法通过0.05到0.5084的大小步长实现了在联合口交（IoU）阈值0.5处的总体溃疡检测平均精度（AP）为0.8632，在联合口交大于（IoU）阈值0.5到0.95处的平均平均精度（mAP） -101骨干。

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