用深度学习自动识别皮肤癌手术切片中的肿瘤与伪影
An ensemble deep learning approach to detect tumors on Mohs micrographic surgery slides
- 基于U-Net的集成模型分割切片中肿瘤与正常区域
- 切片级分类AUC达0.91，病灶检测准确率高
- 可辅助医生快速诊断，提升手术效率
莫氏显微手术（MMS）是治疗高风险非黑色素瘤皮肤癌的金标准，但术中病理检查耗时费力且依赖专业经验。本研究旨在开发深度学习模型，用于检测莫氏切片中的基底细胞癌（BCC）和伪影。共使用51例患者的731张莫氏切片，其中91张含肿瘤，640张无肿瘤。采用基于U-Net的模型对切片进行肿瘤与非肿瘤区域分割，再将分割后的图像块分类，生成全切片图像（WSI）预测结果。分割阶段的Dice分数分别为0.70（肿瘤）和0.67（非肿瘤），肿瘤与非肿瘤的AUC分别达到0.98和0.96。在病灶检测中，基于图像块的分类AUC为0.98，基于全切片的分类AUC为0.91。结果表明该AI系统能高效准确地识别莫氏切片中的肿瘤与非肿瘤区域，有助于提升外科医生和皮肤病理学家的决策精度。
原文摘要 · Abstract (English)
Mohs micrographic surgery (MMS) is the gold standard technique for removing high risk nonmelanoma skin cancer however, intraoperative histopathological examination demands significant time, effort, and professionality. The objective of this study is to develop a deep learning model to detect basal cell carcinoma (BCC) and artifacts on Mohs slides. A total of 731 Mohs slides from 51 patients with BCCs were used in this study, with 91 containing tumor and 640 without tumor which was defined as non-tumor. The dataset was employed to train U-Net based models that segment tumor and non-tumor regions on the slides. The segmented patches were classified as tumor, or non-tumor to produce predictions for whole slide images (WSIs). For the segmentation phase, the deep learning model success was measured using a Dice score with 0.70 and 0.67 value, area under the curve (AUC) score with 0.98 and 0.96 for tumor and non-tumor, respectively. For the tumor classification, an AUC of 0.98 for patch-based detection, and AUC of 0.91 for slide-based detection was obtained on the test dataset. We present an AI system that can detect tumors and non-tumors in Mohs slides with high success. Deep learning can aid Mohs surgeons and dermatopathologists in making more accurate decisions.
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