A Computational Prediction Method Based on Modified U-Net for Cell Distribution in Tumor Microenvironment
文献类型:会议论文
作者 | Bian, Chang1,4; Wang, Yu1,4; An, Yu1,3; Wang, Hanfan1,2; Du, Yang1,4; Tian, Jie1,2,3,4 |
出版日期 | 2021-02 |
会议日期 | 2021-2-15 |
会议地点 | 线上会议 |
英文摘要 | The tumor microenvironment (TME) is the internal environment in which tumors develop and consists of tumor cells, various immune cells, and interstitial cells. Understanding TME can help predict clinical response of immunotherapy and offer guidance for therapeutic optimization. Current pathological practice utilizes multiplexed immunohistochemistry (mIHC) to make assessment of different types of cell distribution in TME. However, these staining methods are not only costly but can also reduce the quality of the sample tissues and staining results often require professional pathologists to interpret, which can be possibly influenced by subjectivity. In this work, we propose a computational prediction method for cell distribution in TME using a modified U-Net structure, which can learn useful features from the hematoxylin and eosin (H&E) images and predict PanCK positive colon cells and tumor infiltrating lymphocytes (TILs) at cellular-level. Our created datasets contain H&E images and cellular-level segmentation labels annotated by board-certified pathologists according to the corresponding registered mIHC images. We combined U-Net and Inception block structure and created a modified U-net network which can predict PanCK positive colon cells and TILs distribution in TME,with the accuracy of 84.6% on test set. Hence, this method shows the potential to make assessment of different types of cell distribution in TME more objectively and efficiently. |
源URL | [http://ir.ia.ac.cn/handle/173211/44325] |
专题 | 自动化研究所_中国科学院分子影像重点实验室 中国科学院自动化研究所 |
通讯作者 | Du, Yang; Tian, Jie |
作者单位 | 1.CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, China 3.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China 4.The University of Chinese Academy of Sciences, Beijing, 100080, China |
推荐引用方式 GB/T 7714 | Bian, Chang,Wang, Yu,An, Yu,et al. A Computational Prediction Method Based on Modified U-Net for Cell Distribution in Tumor Microenvironment[C]. 见:. 线上会议. 2021-2-15. |
入库方式: OAI收割
来源:自动化研究所
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