Northeastern University, Electrical and Computer Engineering, Boston, Massachusetts 02115, USA.
J Biomed Opt. 2011 Mar;16(3):036005. doi: 10.1117/1.3549740.
Reflectance confocal microscopy (RCM) continues to be translated toward the detection of skin cancers in vivo. Automated image analysis may help clinicians and accelerate clinical acceptance of RCM. For screening and diagnosis of cancer, the dermal/epidermal junction (DEJ), at which melanomas and basal cell carcinomas originate, is an important feature in skin. In RCM images, the DEJ is marked by optically subtle changes and features and is difficult to detect purely by visual examination. Challenges for automation of DEJ detection include heterogeneity of skin tissue, high inter-, intra-subject variability, and low optical contrast. To cope with these challenges, we propose a semiautomated hybrid sequence segmentation/classification algorithm that partitions z-stacks of tiles into homogeneous segments by fitting a model of skin layer dynamics and then classifies tile segments as epidermis, dermis, or transitional DEJ region using texture features. We evaluate two different training scenarios: 1. training and testing on portions of the same stack; 2. training on one labeled stack and testing on one from a different subject with similar skin type. Initial results demonstrate the detectability of the DEJ in both scenarios with epidermis/dermis misclassification rates smaller than 10% and average distance from the expert labeled boundaries around 8.5 μm.
反射共焦显微镜（RCM）在体内检测皮肤癌方面的应用不断得到拓展。自动图像分析可能有助于临床医生，并加速 RCM 的临床应用。对于癌症的筛查和诊断，表皮/真皮交界处（DEJ）是皮肤中的一个重要特征，黑色素瘤和基底细胞癌就起源于此。在 RCM 图像中，DEJ 标记为光学上的细微变化和特征，仅通过目视检查很难发现。DEJ 检测自动化的挑战包括皮肤组织的异质性、高个体间和个体内变异性以及低光学对比度。为了应对这些挑战，我们提出了一种半自动混合序列分割/分类算法，该算法通过拟合皮肤层动力学模型将瓦片的 z 堆叠分割成均匀的片段，然后使用纹理特征将瓦片片段分类为表皮、真皮或过渡 DEJ 区域。我们评估了两种不同的训练场景：1. 在同一堆栈的部分上进行训练和测试；2. 在一个标记堆栈上进行训练，并在具有相似皮肤类型的不同受试者的一个堆栈上进行测试。初步结果表明，在这两种情况下都可以检测到 DEJ，表皮/真皮误分类率小于 10%，与专家标记边界的平均距离约为 8.5 μm。
