通过深度卷积神经网络进行子宫内膜癌筛查中细胞团块的临床适用病理诊断系统
Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer Screening via Deep Convolutional Neural Networks.
作者信息
Li Qing, Wang Ruijie, Xie Zhonglin, Zhao Lanbo, Wang Yiran, Sun Chao, Han Lu, Liu Yu, Hou Huilian, Liu Chen, Zhang Guanjun, Shi Guizhi, Zhong Dexing, Li Qiling
机构信息
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Department of Obstetrics and Gynecology, Northwest Women's and Children's Hospital, Xi'an 710061, China.
出版信息
Cancers (Basel). 2022 Aug 25;14(17):4109. doi: 10.3390/cancers14174109.
OBJECTIVES
The soaring demand for endometrial cancer screening has exposed a huge shortage of cytopathologists worldwide. To address this problem, our study set out to establish an artificial intelligence system that automatically recognizes and diagnoses pathological images of endometrial cell clumps (ECCs).
METHODS
We used Li Brush to acquire endometrial cells from patients. Liquid-based cytology technology was used to provide slides. The slides were scanned and divided into malignant and benign groups. We proposed two (a U-net segmentation and a DenseNet classification) networks to identify images. Another four classification networks were used for comparison tests.
RESULTS
A total of 113 (42 malignant and 71 benign) endometrial samples were collected, and a dataset containing 15,913 images was constructed. A total of 39,000 ECCs patches were obtained by the segmentation network. Then, 26,880 and 11,520 patches were used for training and testing, respectively. On the premise that the training set reached 100%, the testing set gained 93.5% accuracy, 92.2% specificity, and 92.0% sensitivity. The remaining 600 malignant patches were used for verification.
CONCLUSIONS
An artificial intelligence system was successfully built to classify malignant and benign ECCs.
目的
对子宫内膜癌筛查的需求激增，暴露了全球细胞病理学家的严重短缺。为了解决这一问题，我们的研究着手建立一个能自动识别和诊断子宫内膜细胞团（ECC）病理图像的人工智能系统。
方法
我们使用Li Brush从患者身上获取子宫内膜细胞。采用液基细胞学技术制作玻片。对玻片进行扫描并分为恶性和良性两组。我们提出了两个网络（一个U-net分割网络和一个DenseNet分类网络）来识别图像。另外使用四个分类网络进行对比测试。
结果
共收集了113份子宫内膜样本（42份恶性和71份良性），构建了一个包含15913张图像的数据集。通过分割网络共获得39000个ECC斑块。然后，分别将26880个和11520个斑块用于训练和测试。在训练集达到100%的前提下，测试集的准确率为93.5%，特异性为92.2%，敏感性为92.0%。其余600个恶性斑块用于验证。
结论
成功构建了一个人工智能系统来对恶性和良性ECC进行分类。
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