Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment | |
Zhengyao Peng1,2; Chang Bian1,2; Yang Du1,2; Jie Tian1,2,3,4 | |
发表期刊 | SPIE |
2022 | |
卷号 | 12039页码:1605-7422 |
摘要 | Evaluation of cancer cell and immune cell distribution in tumor microenvironment (TME) is one of the most important factors for guiding cancer immunotherapy and assessing therapeutic response. Multiplexed immunohistochemistry (mIHC) is often used to obtain the different types of cellular biomarker expression and distribution information in TME, but mIHC is limited by time-consuming and cost-intensive, and pathologists’ objectives etc. In this work, we proposed a deep learning-based modified U-Net (m-Unet), by replacing the original convolution sub-module with a modified block to predict the distribution of several typical cellular biomarkers’ expression and distribution information in TME. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners. The model can extract segmentation information from Hematoxylin and Eosin (H&E) images, and predict the cellular biomarker distributions including panCK for colon cancer cells, CD3 and CD20 for tumor infiltrating lymphocytes (TILs) and DAPI for nucleus. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners and. the performance of the m-Unet is better than the U-Net in this work. The optimal prediction accuracy of m-Unet is 88.3% on the test dataset. In general, this model possesses the potential to assist the clinical TME analysis. |
收录类别 | EI |
七大方向——子方向分类 | 医学影像处理与分析 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56547 |
专题 | 中国科学院分子影像重点实验室 |
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.School of Artificial Intelligence, The University of Chinese Academy of Sciences, Beijing, 100049, China 3.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine Science and Engineering, Beihang University, Beijing 100191, China. 4.School of Life Science and Technology, Xidian University, Xi'an 710071, China. |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhengyao Peng,Chang Bian,Yang Du,et al. A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment[J]. SPIE,2022,12039:1605-7422. |
APA | Zhengyao Peng,Chang Bian,Yang Du,&Jie Tian.(2022).A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment.SPIE,12039,1605-7422. |
MLA | Zhengyao Peng,et al."A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment".SPIE 12039(2022):1605-7422. |
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A deep learning-base(572KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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