Effective Algorithm for Biomedical Image Segmentation
Biomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and medical manipulation. Objects in medical images have different scales, types, complex backgrounds, and similar tissue appearances, making information extraction challenging. To solve this problem...
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| Format: | Article |
| Language: | Russian |
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Educational institution «Belarusian State University of Informatics and Radioelectronics»
2024-06-01
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| Series: | Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki |
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| Online Access: | https://doklady.bsuir.by/jour/article/view/3937 |
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| _version_ | 1839567711228657664 |
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| author | Zhao Di Tang Yi A. B. Gourinovitch |
| author_facet | Zhao Di Tang Yi A. B. Gourinovitch |
| author_sort | Zhao Di |
| collection | DOAJ |
| description | Biomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and medical manipulation. Objects in medical images have different scales, types, complex backgrounds, and similar tissue appearances, making information extraction challenging. To solve this problem, a module is proposed that takes into account the features of images, which will improve the biomedical image segmentation network FE-Net. An integral part of the FE-Net algorithm is the connection skipping mechanism, which ensures the connection and fusion of feature maps from different layers in the encoder and decoder. Features at the encoder level are combined with high-level semantic knowledge at the decoder level. The algorithm establishes connections between feature maps, which is used in medicine for image processing. The proposed method is tested on three public datasets: Kvasir-SEG, CVC-ClinicDB and 2018 Data Science Bowl. Based on the results of the study, it was found that FE-Net demonstrates better performance compared to other methods in terms of Intersection over Union and F1-score. The network under consideration copes more effectively with segmentation details and object boundaries, while maintaining high accuracy. The study was conducted jointly with the Department of Magnetic Resonance Imaging of the N. N. Alexandrov National Oncology Center. Access to the source code of the algorithm and additional technical details is available at https://github.com/tyjcbzd/FE-Net. |
| format | Article |
| id | doaj-art-e9c3cec8b5c744a083e2d8d157e4f389 |
| institution | Matheson Library |
| issn | 1729-7648 |
| language | Russian |
| publishDate | 2024-06-01 |
| publisher | Educational institution «Belarusian State University of Informatics and Radioelectronics» |
| record_format | Article |
| series | Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki |
| spelling | doaj-art-e9c3cec8b5c744a083e2d8d157e4f3892025-08-04T17:38:23ZrusEducational institution «Belarusian State University of Informatics and Radioelectronics»Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki1729-76482024-06-01223849210.35596/1729-7648-2024-22-3-84-921997Effective Algorithm for Biomedical Image SegmentationZhao Di0Tang Yi1A. B. Gourinovitch2Belarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsBiomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and medical manipulation. Objects in medical images have different scales, types, complex backgrounds, and similar tissue appearances, making information extraction challenging. To solve this problem, a module is proposed that takes into account the features of images, which will improve the biomedical image segmentation network FE-Net. An integral part of the FE-Net algorithm is the connection skipping mechanism, which ensures the connection and fusion of feature maps from different layers in the encoder and decoder. Features at the encoder level are combined with high-level semantic knowledge at the decoder level. The algorithm establishes connections between feature maps, which is used in medicine for image processing. The proposed method is tested on three public datasets: Kvasir-SEG, CVC-ClinicDB and 2018 Data Science Bowl. Based on the results of the study, it was found that FE-Net demonstrates better performance compared to other methods in terms of Intersection over Union and F1-score. The network under consideration copes more effectively with segmentation details and object boundaries, while maintaining high accuracy. The study was conducted jointly with the Department of Magnetic Resonance Imaging of the N. N. Alexandrov National Oncology Center. Access to the source code of the algorithm and additional technical details is available at https://github.com/tyjcbzd/FE-Net.https://doklady.bsuir.by/jour/article/view/3937biomedical image segmentationconvolution neural networkfeature aware moduleattention mechanismu-shaped network |
| spellingShingle | Zhao Di Tang Yi A. B. Gourinovitch Effective Algorithm for Biomedical Image Segmentation Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki biomedical image segmentation convolution neural network feature aware module attention mechanism u-shaped network |
| title | Effective Algorithm for Biomedical Image Segmentation |
| title_full | Effective Algorithm for Biomedical Image Segmentation |
| title_fullStr | Effective Algorithm for Biomedical Image Segmentation |
| title_full_unstemmed | Effective Algorithm for Biomedical Image Segmentation |
| title_short | Effective Algorithm for Biomedical Image Segmentation |
| title_sort | effective algorithm for biomedical image segmentation |
| topic | biomedical image segmentation convolution neural network feature aware module attention mechanism u-shaped network |
| url | https://doklady.bsuir.by/jour/article/view/3937 |
| work_keys_str_mv | AT zhaodi effectivealgorithmforbiomedicalimagesegmentation AT tangyi effectivealgorithmforbiomedicalimagesegmentation AT abgourinovitch effectivealgorithmforbiomedicalimagesegmentation |