Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts
Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts
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DOI：
10.1002/lsm.23324
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发表时间：
2020-09-22
影响因子：
2.4
通讯作者：
Baldi, Pierre
中科院分区：
文献类型：
--
作者：
Urban, Gregor;Feil, Nate;Baldi, Pierre
背景和目的开发有效的脱发治疗方法的挑战之一是缺乏可靠的方法来监测治疗反应或脱发进展。在这项研究中，我们提出使用光学相干断层扫描（OCT）和自动深度学习来非侵入性地评估毛发和毛囊计数，这些计数可用于更准确有效地监测毛发生长治疗的成功。研究设计/材料和方法我们收集了14名脱发患者的70次OCT扫描，并训练卷积神经网络（CNN）自动计数扫描中存在的所有卵泡。该模型基于检测毛囊和估计局部毛发密度的双重方法，以便即使在两个或更多个相邻毛发彼此非常接近的情况下也能提供准确的计数。结果我们评估了我们的系统在70 OCT手动标记的扫描在不同的头皮位置从14例，其中20个冗余标记的两个人类专家OCT操作员。当比较个人预测并考虑毛发和毛囊预测的确切位置时，我们发现两个人类评分员在大约22%的毛发和毛囊上彼此不一致。总的来说，深度学习（DL）系统在70次扫描中预测毛囊数量的错误率为11.8%，预测毛发数量的错误率为18.7%。OCT系统可以在三秒内捕获一个头皮位置，DL模型可以在处理扫描后不到一秒内做出所有预测，使用未优化的实现需要半分钟。结论该方法有望成为无创评估患者毛发生长治疗进展的标准，与人工评估相比，节省了大量的时间和精力。激光外科医学（c）2020 Wiley Periodicals，Inc.
Background and Objectives One of the challenges in developing effective hair loss therapies is the lack of reliable methods to monitor treatment response or alopecia progression. In this study, we propose the use of optical coherence tomography (OCT) and automated deep learning to non-invasively evaluate hair and follicle counts that may be used to monitor the success of hair growth therapy more accurately and efficiently. Study Design/Materials and Methods We collected 70 OCT scans from 14 patients with alopecia and trained a convolutional neural network (CNN) to automatically count all follicles present in the scans. The model is based on a dual approach of both detecting hair follicles and estimating the local hair density in order to give accurate counts even for cases where two or more adjacent hairs are in close proximity to each other. Results We evaluate our system on 70 OCT manually labeled scans taken at different scalp locations from 14 patients, with 20 of those redundantly labeled by two human expert OCT operators. When comparing the individual human predictions and considering the exact locations of hair and follicle predictions, we find that the two human raters disagree with each other on approximately 22% of hairs and follicles. Overall, the deep learning (DL) system predicts the number of follicles with an error rate of 11.8% and the number of hairs with an error rate of 18.7% on average on the 70 scans. The OCT system can capture one scalp location in three seconds, and the DL model can make all predictions in less than a second after processing the scan, which takes half a minute using an unoptimized implementation. Conclusion This approach is well-positioned to become the standard for non-invasive evaluation of hair growth treatment progress in patients, saving significant amounts of time and effort compared with manual evaluation. Lasers Surg. Med. (c) 2020 Wiley Periodicals, Inc.