Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model
Kidney tumors represent a type of cancer that people of advanced age are more likely to develop. For this reason, it is important to exercise caution and provide diagnostic tests in the later stages of life. Medical imaging and deep learning methods are becoming increasingly attractive in this sense...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/10/1772 |
| _version_ | 1797551003151630336 |
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| author | Fuat Türk Murat Lüy Necaattin Barışçı |
| author_facet | Fuat Türk Murat Lüy Necaattin Barışçı |
| author_sort | Fuat Türk |
| collection | DOAJ |
| description | Kidney tumors represent a type of cancer that people of advanced age are more likely to develop. For this reason, it is important to exercise caution and provide diagnostic tests in the later stages of life. Medical imaging and deep learning methods are becoming increasingly attractive in this sense. Developing deep learning models to help physicians identify tumors with successful segmentation is of great importance. However, not many successful systems exist for soft tissue organs, such as the kidneys and the prostate, of which segmentation is relatively difficult. In such cases where segmentation is difficult, V-Net-based models are mostly used. This paper proposes a new hybrid model using the superior features of existing V-Net models. The model represents a more successful system with improvements in the encoder and decoder phases not previously applied. We believe that this new hybrid V-Net model could help the majority of physicians, particularly those focused on kidney and kidney tumor segmentation. The proposed model showed better performance in segmentation than existing imaging models and can be easily integrated into all systems due to its flexible structure and applicability. The hybrid V-Net model exhibited average Dice coefficients of 97.7% and 86.5% for kidney and tumor segmentation, respectively, and, therefore, could be used as a reliable method for soft tissue organ segmentation. |
| first_indexed | 2024-03-10T15:38:34Z |
| format | Article |
| id | doaj.art-72cd5c58a0a648f9b109a520afbac91a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T15:38:34Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-72cd5c58a0a648f9b109a520afbac91a2023-11-20T17:01:03ZengMDPI AGMathematics2227-73902020-10-01810177210.3390/math8101772Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based ModelFuat Türk0Murat Lüy1Necaattin Barışçı2Computer Engineering, Graduate School of Natural and Applied Sciences, Kirikkale University, Kirikkale 71451, TurkeyElectrica & Electronics Engineering, Graduate School of Natural and Applied Sciences, University of Kirikkale, Kirikkale 71451, TurkeyComputer Engineering, Faculty of Technology, Gazi University, Ankara 06560, TurkeyKidney tumors represent a type of cancer that people of advanced age are more likely to develop. For this reason, it is important to exercise caution and provide diagnostic tests in the later stages of life. Medical imaging and deep learning methods are becoming increasingly attractive in this sense. Developing deep learning models to help physicians identify tumors with successful segmentation is of great importance. However, not many successful systems exist for soft tissue organs, such as the kidneys and the prostate, of which segmentation is relatively difficult. In such cases where segmentation is difficult, V-Net-based models are mostly used. This paper proposes a new hybrid model using the superior features of existing V-Net models. The model represents a more successful system with improvements in the encoder and decoder phases not previously applied. We believe that this new hybrid V-Net model could help the majority of physicians, particularly those focused on kidney and kidney tumor segmentation. The proposed model showed better performance in segmentation than existing imaging models and can be easily integrated into all systems due to its flexible structure and applicability. The hybrid V-Net model exhibited average Dice coefficients of 97.7% and 86.5% for kidney and tumor segmentation, respectively, and, therefore, could be used as a reliable method for soft tissue organ segmentation.https://www.mdpi.com/2227-7390/8/10/1772medical image segmentationrenal segmentationcomputed tomographykidney cancerhybrid V-Net model |
| spellingShingle | Fuat Türk Murat Lüy Necaattin Barışçı Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model Mathematics medical image segmentation renal segmentation computed tomography kidney cancer hybrid V-Net model |
| title | Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model |
| title_full | Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model |
| title_fullStr | Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model |
| title_full_unstemmed | Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model |
| title_short | Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model |
| title_sort | kidney and renal tumor segmentation using a hybrid v net based model |
| topic | medical image segmentation renal segmentation computed tomography kidney cancer hybrid V-Net model |
| url | https://www.mdpi.com/2227-7390/8/10/1772 |
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