Segmenting skin ulcers and measuring the wound area using deep convolutional networks
Segmenting skin ulcers and measuring the wound area using deep convolutional networks
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DOI：
10.1016/j.cmpb.2020.105376
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发表时间：
2020-07-01
影响因子：
6.1
通讯作者：
Traina, Agma J. M.
中科院分区：
文献类型：
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作者：
Chino, Daniel Y. T.;Scabora, Lucas C.;Traina, Agma J. M.
背景和目的：长期卧床的慢性皮肤溃疡患者通常需要在家中进行检查。医疗保健专业人员通过定期拍摄伤口照片来跟踪患者病情的演变，因为伤口的不同方面可以指示溃疡的愈合阶段，包括深度，位置和大小。伤口尺寸的手动测量通常是不准确的、耗时的，并且还可能引起患者的不适。在这项工作中，我们提出了自动皮肤溃疡区域评估ASURA框架，以准确分割伤口并自动测量其大小。方法：ASURA使用编码器/解码器深度神经网络进行分割，检测图像中存在的测量尺/带并估计其像素密度。结果：实验结果表明，ASURA在Dice评分方面优于最先进的方法高达16%，能够正确分割伤口，Dice评分高于90%。ASURA自动估计图像的像素密度，相对误差为5%。当使用半自动方法时，ASURA能够以平方厘米为单位估计伤口面积，相对误差为14%.Conclusions：结果表明，ASURA非常适合皮肤溃疡的分割和自动测量问题。(C)2020爱思唯尔B. V.保留所有权利。
Background and objectives: Bedridden patients presenting chronic skin ulcers often need to be examined at home. Healthcare professionals follow the evolution of the patients' condition by regularly taking pictures of the wounds, as different aspects of the wound can indicate the healing stages of the ulcer, including depth, location, and size. The manual measurement of the wounds' size is often inaccurate, time-consuming, and can also cause discomfort to the patient. In this work, we propose the Automatic Skin Ulcer Region Assessment ASURA framework to accurately segment the wound and automatically measure its size.Methods: ASURA uses an encoder/decoder deep neural network to perform the segmentation, which detects the measurement ruler/tape present in the image and estimates its pixel density.Results: Experimental results show that ASURA outperforms the state-of-the-art methods by up to 16% regarding the Dice score, being able to correctly segment the wound with a Dice score higher than 90%. ASURA automatically estimates the pixel density of the images with a relative error of 5%. When using a semi-automatic approach, ASURA was able to estimate the area of the wound in square centimeters with a relative error of 14%.Conclusions: The results show that ASURA is well-suited for the problem of segmenting and automatically measuring skin ulcers. (C) 2020 Elsevier B.V. All rights reserved.