Deep learning approach to skin layers segmentation in inflammatory dermatoses
Deep learning approach to skin layers segmentation in inflammatory dermatoses
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基于深度学习的炎症性皮肤病皮肤层分割方法
DOI：
10.1016/j.ultras.2021.106412
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发表时间：
2021-03-27
期刊：
影响因子：
4.2
通讯作者：
Platkowska-Szczerek, Anna
中科院分区：
文献类型：
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作者：
Czajkowska, Joanna;Badura, Pawel;Platkowska-Szczerek, Anna
通过医学成像监测皮肤层对于诊断和治疗慢性炎症性皮肤病患者至关重要。高频超声波 (HFUS) 可以监测不同皮肤病的皮肤状况。对特应性皮炎或牛皮癣患者的皮肤层进行准确可靠的分割，可以通过层厚度测量来评估治疗效果。表皮和表皮下低回声带（SLEB）对于进一步诊断最重要，因为它们的外观是不同皮肤问题的指标。在医疗实践中，包括分割在内的分析通常由医生手动执行，但这种方法存在所有缺点，例如耗时长和缺乏可重复性。近年来，HFUS 在皮肤病学实践中变得普遍，但几乎没有自动化分析工具的发展支持。为了满足皮肤层分割和测量的需求，我们开发了表皮层和SLEB层的自动分割方法。它由基于模糊 cmeans 聚类的预处理步骤和 U 形卷积神经网络组成。该网络采用批量归一化层来调整和缩放激活，以使分割更加稳健。所获得的分割结果经过验证，并与当前解决皮肤层分割的最先进方法进行比较。获得的表皮和 SLEB 的 Dice 系数分别等于 0.87 和 0.83，证明了所开发框架的效率，优于其他方法。
Monitoring skin layers with medical imaging is critical to diagnosing and treating patients with chronic inflammatory skin diseases. The high-frequency ultrasound (HFUS) makes it possible to monitor skin condition in different dermatoses. Accurate and reliable segmentation of skin layers in patients with atopic dermatitis or psoriasis enables the assessment of the treatment effect by the layer thickness measurements. The epidermis and the subepidermal low echogenic band (SLEB) are the most important for further diagnosis since their appearance is an indicator of different skin problems. In medical practice, the analysis, including segmentation, is usually performed manually by the physician with all drawbacks of such an approach, e.g., extensive time consumption and lack of repeatability. Recently, HFUS becomes common in dermatological practice, yet it is barely supported by the development of automated analysis tools. To meet the need for skin layer segmentation and measurement, we developed an automated segmentation method of both epidermis and SLEB layers. It consists of a fuzzy cmeans clustering-based preprocessing step followed by a U-shaped convolutional neural network. The network employs batch normalization layers adjusting and scaling the activation to make the segmentation more robust. The obtained segmentation results are verified and compared to the current state-of-the-art methods addressing the skin layer segmentation. The obtained Dice coefficient equal to 0.87 and 0.83 for the epidermis and SLEB, respectively, proves the developed framework?s efficiency, outperforming the other approaches.