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 c-means 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.

用医学成像监测皮肤层对于诊断和治疗患有慢性炎症性皮肤病的患者至关重要。高频超声（HFUS）使得监测不同皮肤病的皮肤状况成为可能。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，可以通过测量层厚度来评估治疗效果。表皮和表皮下低回声带（SLEB）对于进一步诊断最为重要，因为它们的出现是不同皮肤问题的指标。在医学实践中，包括分段在内的分析通常是由医生手动执行的，但存在这种方法的所有缺点，例如，大量的时间消耗和缺乏可重复性。最近，HFUS在皮肤病学实践中变得很普遍，但是自动化分析工具的开发几乎不支持它。为了满足皮肤层分割和测量的需要，我们开发了一种对表皮和SLEB层进行自动分割的方法。它包括一个基于模糊c均值聚类的预处理步骤，然后是一个U形卷积神经网络。该网络采用批处理归一化层来调整和缩放激活，以使分段更加可靠。验证所获得的分割结果，并将其与解决皮肤层分割的当前最新方法进行比较。所获得的骰子系数等于 它包括一个基于模糊c均值聚类的预处理步骤，然后是一个U形卷积神经网络。该网络采用批处理归一化层来调整和缩放激活，以使分段更加可靠。验证所获得的分割结果，并将其与解决皮肤层分割的当前最新方法进行比较。所获得的骰子系数等于 它包括一个基于模糊c均值聚类的预处理步骤，然后是一个U形卷积神经网络。该网络采用批处理归一化层来调整和缩放激活，以使分段更加可靠。验证所获得的分割结果，并将其与解决皮肤层分割的当前最新方法进行比较。所获得的骰子系数等于$0.87$ 和 $0.83$ 表皮和SLEB分别证明了开发框架的效率，优于其他方法。