Reliable Detection of Eczema Areas for Fully Automated Assessment of Eczema Severity from Digital Camera Images.
Reliable Detection of Eczema Areas for Fully Automated Assessment of Eczema Severity from Digital Camera Images.
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DOI：
10.1016/j.xjidi.2023.100213
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发表时间：
2023-09
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影响因子：
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通讯作者：
Tanaka, Reiko J
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作者：
Attar, Rahman;Hurault, Guillem;Wang, Zihao;Mokhtari, Ricardo;Pan, Kevin;Olabi, Bayanne;Earp, Eleanor;Steele, Lloyd;Williams, Hywel C;Tanaka, Reiko J
在临床研究中评估湿疹的严重程度需要由训练有素的工作人员进行面对面的皮肤检查。这种方法对于参与者和工作人员来说是资源密集型的，在大流行期间具有挑战性，并且容易出现观察者之间和观察者内部的差异。计算机视觉算法已经被提出来使用数码相机图像自动评估湿疹严重程度。然而，它们通常需要人为干预来检测湿疹病变，并且不能在端到端管道中从真实世界图像自动评估湿疹严重程度。我们开发了一个模型，使用皮肤科医生提供的1，345张图像上的湿疹病变的数据增强和像素级分割来从图像中检测湿疹病变。我们评估了所获得的分割质量与临床医生的分割质量相比，对现实图像中遇到的不同成像条件（如照明、焦点和模糊）的鲁棒性，以及使用检测到的湿疹病变时下游严重程度预测的性能。与我们以前的湿疹检测模型相比，湿疹病变检测的质量和鲁棒性分别提高了约25%和40%。下游严重度预测的性能保持不变。使用皮肤分割作为需要专家标记的湿疹分割的替代方案，显示出与使用湿疹分割时相同的性能。
Assessing the severity of eczema in clinical research requires face-to-face skin examination by trained staff. Such approaches are resource-intensive for participants and staff, challenging during pandemics, and prone to inter- and intra-observer variation. Computer vision algorithms have been proposed to automate the assessment of eczema severity using digital camera images. However, they often require human intervention to detect eczema lesions and cannot automatically assess eczema severity from real-world images in an end-to-end pipeline. We developed a model to detect eczema lesions from images using data augmentation and pixel-level segmentation of eczema lesions on 1,345 images provided by dermatologists. We evaluated the quality of the obtained segmentation compared with that of the clinicians, the robustness to varying imaging conditions encountered in real-life images, such as lighting, focus, and blur, and the performance of downstream severity prediction when using the detected eczema lesions. The quality and robustness of eczema lesion detection increased by approximately 25% and 40%, respectively, compared with that of our previous eczema detection model. The performance of the downstream severity prediction remained unchanged. Use of skin segmentation as an alternative to eczema segmentation that requires specialist labeling showed the performance on par with when eczema segmentation is used.