对于皮肤科实践，非标准化的常规照片图像被拍摄和收集为图像视野的可变领域的混合物，包括聚焦于指定的病变的近距离图像和包括正常皮肤和身体表面背景的远距离图像。使用非标准化的常规照片图像训练的计算机辅助检测/诊断（CAD）模型的表现率低于检测限于局部小区域（如皮肤显微镜图像）的病变的CAD模型。我们旨在开发一个卷积神经网络（CNN）模型，用于皮肤图像分割，以生成适用于多种皮肤疾病分类CAD的皮肤疾病图像数据集。我们训练了一个基于DeepLabv3+的CNN分割模型，以检测皮肤和病变区域，并分割出满足以下条件的区域：图像的80%以上将是皮肤区域，而10%以上将是病变区域。利用CAD对皮肤疾病分类进行检查，生成的CNN分割图像数据库实现了大约90%的敏感度和特异度，可以区分过敏性皮炎和恶性疾病及其并发症的如真菌性红斑病、脓疱病和单纯疱疹感染。CNN分割图像数据集中皮肤疾病分类的准确性几乎与手动裁剪图像数据集相等，并高于原始图像数据集。我们的CNN分割模型不受图像领域的限制，自动提取病变和皮肤区域分割图像，将减轻医师注释的负担并提高CAD的性能。版权所有2023年日本皮肤研究学会。Elsevier B.V.保留所有权利。

For dermatological practices, non-standardized conventional photo images are taken and collected as a mixture of variable fields of the image view, including close-up images focusing on designated lesions and long-shot images including normal skin and background of the body surface. Computer-aided detection/diagnosis (CAD) models trained using non-standardized conventional photo images exhibit lower performance rates than CAD models that detect lesions in a localized small area, such as dermoscopic images.We aimed to develop a convolutional neural network (CNN) model for skin image segmentation to generate a skin disease image dataset suitable for CAD of multiple skin disease classification.We trained a DeepLabv3 + -based CNN segmentation model to detect skin and lesion areas and segmented out areas that satisfy the following conditions: more than 80% of the image will be the skin area, and more than 10% of the image will be the lesion area.The generated CNN-segmented image database was examined using CAD of skin disease classification and achieved approximately 90% sensitivity and specificity to differentiate atopic dermatitis from malignant diseases and complications, such as mycosis fungoides, impetigo, and herpesvirus infection. The accuracy of skin disease classification in the CNN-segmented image dataset was almost equal to that of the manually cropped image dataset and higher than that of the original image dataset.Our CNN segmentation model, which automatically extracts lesions and segmented images of the skin area regardless of image fields, will reduce the burden of physician annotation and improve CAD performance.Copyright © 2023 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.