VLSM-Net: A Fusion Architecture for CT Image Segmentation
Region of interest (ROI) segmentation is a key step in computer-aided diagnosis (CAD). With the problems of blurred tissue edges and imprecise boundaries of ROI in medical images, it is hard to extract satisfactory ROIs from medical images. In order to overcome the shortcomings in segmentation from...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4384 |
| _version_ | 1797608301835321344 |
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| author | Yachun Gao Jia Guo Chuanji Fu Yan Wang Shimin Cai |
| author_facet | Yachun Gao Jia Guo Chuanji Fu Yan Wang Shimin Cai |
| author_sort | Yachun Gao |
| collection | DOAJ |
| description | Region of interest (ROI) segmentation is a key step in computer-aided diagnosis (CAD). With the problems of blurred tissue edges and imprecise boundaries of ROI in medical images, it is hard to extract satisfactory ROIs from medical images. In order to overcome the shortcomings in segmentation from the V-Net model or the level set method (LSM), we propose in this paper a new image segmentation method, the VLSM-Net model, combining these two methods. Specifically, we first use the V-Net model to segment the ROIs, and set the segmentation result as the initial contour. It is then fed through the hybrid LSM for further fine segmentation. That is, the complete segmentation of the V-Net model can be obtained by successively combining the V-Net model and the hybrid LSM. The experimental results conducted in the public datasets LiTS and LUNA show that, compared with the V-Net model or LSM alone, our VLSM-Net model greatly improves the sensitivity, precision and dice coefficient values (DCV) in 3D image segmentation, thus validating our model’s effectiveness. |
| first_indexed | 2024-03-11T05:42:33Z |
| format | Article |
| id | doaj.art-15f877abc9ee412391a4bdf85a23f0ed |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:42:33Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-15f877abc9ee412391a4bdf85a23f0ed2023-11-17T16:19:35ZengMDPI AGApplied Sciences2076-34172023-03-01137438410.3390/app13074384VLSM-Net: A Fusion Architecture for CT Image SegmentationYachun Gao0Jia Guo1Chuanji Fu2Yan Wang3Shimin Cai4School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Physics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Physics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaBig Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, ChinaBig Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, ChinaRegion of interest (ROI) segmentation is a key step in computer-aided diagnosis (CAD). With the problems of blurred tissue edges and imprecise boundaries of ROI in medical images, it is hard to extract satisfactory ROIs from medical images. In order to overcome the shortcomings in segmentation from the V-Net model or the level set method (LSM), we propose in this paper a new image segmentation method, the VLSM-Net model, combining these two methods. Specifically, we first use the V-Net model to segment the ROIs, and set the segmentation result as the initial contour. It is then fed through the hybrid LSM for further fine segmentation. That is, the complete segmentation of the V-Net model can be obtained by successively combining the V-Net model and the hybrid LSM. The experimental results conducted in the public datasets LiTS and LUNA show that, compared with the V-Net model or LSM alone, our VLSM-Net model greatly improves the sensitivity, precision and dice coefficient values (DCV) in 3D image segmentation, thus validating our model’s effectiveness.https://www.mdpi.com/2076-3417/13/7/4384CT imageconvolutional neural networkV-Net modellevel set methodliver segmentation |
| spellingShingle | Yachun Gao Jia Guo Chuanji Fu Yan Wang Shimin Cai VLSM-Net: A Fusion Architecture for CT Image Segmentation Applied Sciences CT image convolutional neural network V-Net model level set method liver segmentation |
| title | VLSM-Net: A Fusion Architecture for CT Image Segmentation |
| title_full | VLSM-Net: A Fusion Architecture for CT Image Segmentation |
| title_fullStr | VLSM-Net: A Fusion Architecture for CT Image Segmentation |
| title_full_unstemmed | VLSM-Net: A Fusion Architecture for CT Image Segmentation |
| title_short | VLSM-Net: A Fusion Architecture for CT Image Segmentation |
| title_sort | vlsm net a fusion architecture for ct image segmentation |
| topic | CT image convolutional neural network V-Net model level set method liver segmentation |
| url | https://www.mdpi.com/2076-3417/13/7/4384 |
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