CASIA OpenIR  > 中国科学院分子影像重点实验室
Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging
Xiao, Anqi1,2,3; Shen, Biluo1,2,3; Shi, Xiaojing1,2,3; Zhang, Zhe4,5; Zhang, Zeyu1,2,6; Tian, Jie1,2,3,6,7; Ji, Nan4,5,6; Hu, Zhenhua1,2,3
Source PublicationIEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN0278-0062
2022-10-01
Volume41Issue:10Pages:2570-2581
Corresponding AuthorTian, Jie(tian@ieee.org) ; Ji, Nan(jinan@bjtth.org) ; Hu, Zhenhua(zhenhua.hu@ia.ac.cn)
AbstractGlioma grading during surgery can help clinical treatment planning and prognosis, but intraoperative pathological examination of frozen sections is limited by the long processing time and complex procedures. Near-infrared fluorescence imaging provides chances for fast and accurate real-time diagnosis. Recently, deep learning techniques have been actively explored for medical image analysis and disease diagnosis. However, issues of near-infrared fluorescence images, including small-scale, noise, and low-resolution, increase the difficulty of training a satisfying network. Multi-modal imaging can provide complementary information to boost model performance, but simultaneously designing a proper network and utilizing the information of multi-modal data is challenging. In this work, we propose a novel neural architecture search method DLS-DARTS to automatically search for network architectures to handle these issues. DLS-DARTS has two learnable stems for multi-modal low-level feature fusion and uses a modified perturbation-based derivation strategy to improve the performance on the area under the curve and accuracy. White light imaging and fluorescence imaging in the first near-infrared window (650-900 nm) and the second near-infrared window (1,000-1,700 nm) are applied to provide multi-modal information on glioma tissues. In the experiments on 1,115 surgical glioma specimens, DLS-DARTS achieved an area under the curve of 0.843 and an accuracy of 0.634, which outperformed manually designed convolutional neural networks including ResNet, PyramidNet, and EfficientNet, and a state-of-the-art neural architecture search method for multi-modal medical image classification. Our study demonstrates that DLS-DARTS has the potential to help neurosurgeons during surgery, showing high prospects in medical image analysis.
KeywordImaging Computer architecture Fluorescence Feature extraction Surgery Biomedical imaging Medical diagnostic imaging Deep learning glioma grading intraoperative imaging multi-modal imaging neural architecture search NIR-II fluorescence imaging
DOI10.1109/TMI.2022.3166129
WOS KeywordCENTRAL-NERVOUS-SYSTEM ; CLASSIFICATION ; TUMORS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0205200] ; National Natural Science Foundation of China (NSFC)[62027901] ; National Natural Science Foundation of China (NSFC)[81930053] ; National Natural Science Foundation of China (NSFC)[92059207] ; National Natural Science Foundation of China (NSFC)[81227901] ; Beijing Natural Science Foundation[JQ19027] ; CAS Youth Interdisciplinary Team[JCTD-2021-08] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA16021200] ; Zhuhai High-Level Health Personnel Team Project[Zhuhai HLHPTP201703] ; Innovative Research Team of High-Level Local Universities in Shanghai
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Beijing Natural Science Foundation ; CAS Youth Interdisciplinary Team ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Zhuhai High-Level Health Personnel Team Project ; Innovative Research Team of High-Level Local Universities in Shanghai
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000862400100003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50332
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie; Ji, Nan; Hu, Zhenhua
Affiliation1.Chinese Acad Sci, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing 100070, Peoples R China
5.Beijing Tiantan Hosp, China Natl Clin Res Ctr Neurol Dis, Beijing 100070, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging Minist Educ, Sch Life Sci & Technol, Xian 710071, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xiao, Anqi,Shen, Biluo,Shi, Xiaojing,et al. Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2022,41(10):2570-2581.
APA Xiao, Anqi.,Shen, Biluo.,Shi, Xiaojing.,Zhang, Zhe.,Zhang, Zeyu.,...&Hu, Zhenhua.(2022).Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging.IEEE TRANSACTIONS ON MEDICAL IMAGING,41(10),2570-2581.
MLA Xiao, Anqi,et al."Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging".IEEE TRANSACTIONS ON MEDICAL IMAGING 41.10(2022):2570-2581.
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