慢性伤口图像中照相伤口评估工具的自动预测,Journal of Medical Systems

文献中已经提出了许多基于图像处理分析来量化临床相关伤口特征的自动化方法，旨在消除人为主观性并加速临床实践。在这项工作中，我们提出了一个完全自动化的图像处理流程，利用深度学习和大型伤口分割数据集来执行伤口检测并跟踪摄影伤口评估工具（PWAT）的预测，从而自动对伤口充分愈合进行临床判断。从智能手机摄像头获取的图像开始，从伤口区域提取一系列纹理和形态特征，旨在模仿伤口评估的典型临床考虑因素。临床医生可以轻松解释所提取的特征，并可以定量估计 PWAT 分数。从我们预先训练的神经网络模型检测到的感兴趣区域中提取的特征可以正确预测一组未见过的图像上的 PWAT 比例值，Spearman 相关系数为 0.85。获得的结果与当前的最先进水平一致，并为该研究领域的未来人工智能应用提供了基准。

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Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.