Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning
Osteosarcoma is the most common primary bone tumor with high malignancy. It is particularly necessary to achieve rapid and accurate diagnosis in its intraoperative examination and early diagnosis. Accordingly, the multimodal microscopic imaging diagnosis system constructed by bright field, spontaneo...
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| Format: | Article |
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World Scientific Publishing
2025-03-01
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| Series: | Journal of Innovative Optical Health Sciences |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S1793545823430010 |
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| author | Zihan Wang Jinjin Wu Chenbei Li Bing Wang Qingxia Wu Lan Li Huijie Wang Chao Tu Jianhua Yin |
| author_facet | Zihan Wang Jinjin Wu Chenbei Li Bing Wang Qingxia Wu Lan Li Huijie Wang Chao Tu Jianhua Yin |
| author_sort | Zihan Wang |
| collection | DOAJ |
| description | Osteosarcoma is the most common primary bone tumor with high malignancy. It is particularly necessary to achieve rapid and accurate diagnosis in its intraoperative examination and early diagnosis. Accordingly, the multimodal microscopic imaging diagnosis system constructed by bright field, spontaneous fluorescence and polarized light microscopic imaging was used to study the pathological mechanism of osteosarcoma from the tissue microenvironment level and achieve rapid and accurate diagnosis. First, the multimodal microscopic images of normal and osteosarcoma tissue slices were collected to characterize the overall morphology of the tissue microenvironment of the samples, the arrangement structure of collagen fibers and the content and distribution of endogenous fluorescent substances. Second, based on the correlation and complementarity of the feature information contained in the three single-mode images, combined with convolutional neural network (CNN) and image fusion methods, a multimodal intelligent diagnosis model was constructed to effectively improve the information utilization and diagnosis accuracy. The accuracy and true positivity of the multimodal diagnostic model were significantly improved to 0.8495 and 0.9412, respectively, compared to those of the single-modal models. Besides, the difference of tissue microenvironments before and after cancerization can be used as a basis for cancer diagnosis, and the information extraction and intelligent diagnosis of osteosarcoma tissue can be achieved by using multimodal microscopic imaging technology combined with deep learning, which significantly promoted the application of tissue microenvironment in pathological examination. This diagnostic system relies on its advantages of simple operation, high efficiency and accuracy and high cost-effectiveness, and has enormous clinical application potential and research significance. |
| format | Article |
| id | doaj-art-51181a10a3d94e7bb0a88b74cc63ff0b |
| institution | DOAJ |
| issn | 1793-5458 1793-7205 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj-art-51181a10a3d94e7bb0a88b74cc63ff0b2025-08-20T02:54:23ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052025-03-01180210.1142/S1793545823430010Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learningZihan Wang0Jinjin Wu1Chenbei Li2Bing Wang3Qingxia Wu4Lan Li5Huijie Wang6Chao Tu7Jianhua Yin8Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. ChinaDepartment of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. ChinaDepartment of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, P. R. ChinaDepartment of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. ChinaDepartment of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. ChinaDepartment of Pathology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, P. R. ChinaDepartment of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. ChinaDepartment of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, P. R. ChinaDepartment of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. ChinaOsteosarcoma is the most common primary bone tumor with high malignancy. It is particularly necessary to achieve rapid and accurate diagnosis in its intraoperative examination and early diagnosis. Accordingly, the multimodal microscopic imaging diagnosis system constructed by bright field, spontaneous fluorescence and polarized light microscopic imaging was used to study the pathological mechanism of osteosarcoma from the tissue microenvironment level and achieve rapid and accurate diagnosis. First, the multimodal microscopic images of normal and osteosarcoma tissue slices were collected to characterize the overall morphology of the tissue microenvironment of the samples, the arrangement structure of collagen fibers and the content and distribution of endogenous fluorescent substances. Second, based on the correlation and complementarity of the feature information contained in the three single-mode images, combined with convolutional neural network (CNN) and image fusion methods, a multimodal intelligent diagnosis model was constructed to effectively improve the information utilization and diagnosis accuracy. The accuracy and true positivity of the multimodal diagnostic model were significantly improved to 0.8495 and 0.9412, respectively, compared to those of the single-modal models. Besides, the difference of tissue microenvironments before and after cancerization can be used as a basis for cancer diagnosis, and the information extraction and intelligent diagnosis of osteosarcoma tissue can be achieved by using multimodal microscopic imaging technology combined with deep learning, which significantly promoted the application of tissue microenvironment in pathological examination. This diagnostic system relies on its advantages of simple operation, high efficiency and accuracy and high cost-effectiveness, and has enormous clinical application potential and research significance.https://www.worldscientific.com/doi/10.1142/S1793545823430010Multimodal imagingimage fusiondeep learningosteosarcomaintelligent diagnosis |
| spellingShingle | Zihan Wang Jinjin Wu Chenbei Li Bing Wang Qingxia Wu Lan Li Huijie Wang Chao Tu Jianhua Yin Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning Journal of Innovative Optical Health Sciences Multimodal imaging image fusion deep learning osteosarcoma intelligent diagnosis |
| title | Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning |
| title_full | Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning |
| title_fullStr | Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning |
| title_full_unstemmed | Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning |
| title_short | Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning |
| title_sort | diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning |
| topic | Multimodal imaging image fusion deep learning osteosarcoma intelligent diagnosis |
| url | https://www.worldscientific.com/doi/10.1142/S1793545823430010 |
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