IEEE J Biomed Health Inform. 2024 Mar;28(3):1623-1634. doi: 10.1109/JBHI.2023.3342069. Epub 2024 Mar 6.
Quantitative evaluation of vitiligo is crucial for assessing treatment response. Dermatologists evaluate vitiligo regularly to adjust their treatment plans, which requires extra work. Furthermore, the evaluations may not be objective due to inter- and intra-assessor variability. Though automatic vitiligo segmentation methods provide an objective evaluation, previous methods mainly focus on patch-wise images, and their results cannot be translated into clinical scores for treatment adjustment. Thus, full-body vitiligo segmentation needs to be developed for recording vitiligo changes in different body parts of a patient and for calculating the clinical scores. To bridge this gap, the first full-body vitiligo dataset with 1740 images, following the international vitiligo photo standard, was established. Compared with patch-wise images, full-body images have more complicated ambient light conditions and larger variances in lesion size and distribution. Additionally, in some hand and foot images, skin can be fully covered by either vitiligo or healthy skin. Previous patch-wise segmentation studies completely ignore these cases, as they assume that the contrast between vitiligo and healthy skin is available in each image for segmentation. To address the aforementioned challenges, the proposed algorithm in this study exploits a tailor-made contrast enhancement scheme and long-range comparison. Furthermore, a novel confidence score refinement module is proposed to manage images fully covered by vitiligo or healthy skin. Our results can be converted to clinical scores and used by clinicians. Compared to the state-of-the-art method, the proposed algorithm reduces the average per-image vitiligo involvement percentage error from 3.69% to 1.81%, and the top 10% per-image errors from 23.17% to 8.29%. Our algorithm achieves 1.17% and 3.11% for the mean and max error for the per-patient vitiligo involvement percentage, which is better than an experienced dermatologist's naked-eye evaluation.
量化评估白癜风对于评估治疗反应至关重要。皮肤科医生定期评估白癜风以调整治疗计划，这需要额外的工作。此外，由于评估者之间和评估者内部的可变性，评估可能不够客观。虽然自动白癜风分割方法提供了客观的评估，但以前的方法主要集中在斑块图像上，其结果无法转化为用于治疗调整的临床评分。因此，需要开发全身白癜风分割方法，以记录患者不同身体部位的白癜风变化并计算临床评分。为了弥补这一差距，建立了第一个包含 1740 张图像的全身白癜风数据集，符合国际白癜风照片标准。与斑块图像相比，全身图像具有更复杂的环境光条件，病变大小和分布的差异更大。此外，在一些手和脚图像中，皮肤可以完全被白癜风或健康皮肤覆盖。以前的斑块分割研究完全忽略了这些情况，因为它们假设每个图像中都存在白癜风和健康皮肤之间的对比度，可用于分割。为了解决上述挑战，本研究提出的算法利用了定制的对比度增强方案和长程比较。此外，还提出了一种新颖的置信度评分细化模块来管理完全被白癜风或健康皮肤覆盖的图像。我们的结果可以转换为临床评分并由临床医生使用。与最先进的方法相比，所提出的算法将平均每张图像的白癜风受累百分比误差从 3.69%降低到 1.81%，将每张图像的前 10%误差从 23.17%降低到 8.29%。对于每位患者的白癜风受累百分比，我们的算法的平均值和最大值误差分别为 1.17%和 3.11%，优于经验丰富的皮肤科医生的肉眼评估。
