Li Shuo, Wang He, Xiao Yiding, Zhang Mingzi, Yu Nanze, Zeng Ang, Wang Xiaojun
Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Department of Neurological Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
J Pers Med. 2022 Jun 16;12(6):981. doi: 10.3390/jpm12060981.
A keloid results from abnormal wound healing, which has different blood perfusion and growth states among patients. Active monitoring and treatment of actively growing keloids at the initial stage can effectively inhibit keloid enlargement and has important medical and aesthetic implications. LSCI (laser speckle contrast imaging) has been developed to obtain the blood perfusion of the keloid and shows a high relationship with the severity and prognosis. However, the LSCI-based method requires manual annotation and evaluation of the keloid, which is time consuming. Although many studies have designed deep-learning networks for the detection and classification of skin lesions, there are still challenges to the assessment of keloid growth status, especially based on small samples. This retrospective study included 150 untreated keloid patients, intensity images, and blood perfusion images obtained from LSCI. A newly proposed workflow based on cascaded vision transformer architecture was proposed, reaching a dice coefficient value of 0.895 for keloid segmentation by 2% improvement, an error of 8.6 ± 5.4 perfusion units, and a relative error of 7.8% ± 6.6% for blood calculation, and an accuracy of 0.927 for growth state prediction by 1.4% improvement than baseline.
瘢痕疙瘩是由异常伤口愈合引起的，不同患者之间其血液灌注和生长状态存在差异。在初始阶段对活跃生长的瘢痕疙瘩进行积极监测和治疗可有效抑制瘢痕疙瘩增大，具有重要的医学和美学意义。激光散斑对比成像（LSCI）已被开发用于获取瘢痕疙瘩的血液灌注情况，且与严重程度和预后高度相关。然而，基于LSCI的方法需要对瘢痕疙瘩进行手动标注和评估，这很耗时。尽管许多研究设计了用于皮肤病变检测和分类的深度学习网络，但对瘢痕疙瘩生长状态的评估，尤其是基于小样本的评估，仍然存在挑战。这项回顾性研究纳入了150例未经治疗的瘢痕疙瘩患者、强度图像以及从LSCI获得的血液灌注图像。提出了一种基于级联视觉Transformer架构的新工作流程，瘢痕疙瘩分割的骰子系数值达到0.895，提高了2%，血液计算的误差为8.6±5.4灌注单位，相对误差为7.8%±6.6%，生长状态预测的准确率为0.927，比基线提高了1.4%。
