Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks
Abstract The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients’ overall survival rate. Ther...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84386-0 |
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| author | Deependra Rastogi Prashant Johri Massimo Donelli Seifedine Kadry Arfat Ahmad Khan Giuseppe Espa Paola Feraco Jungeun Kim |
| author_facet | Deependra Rastogi Prashant Johri Massimo Donelli Seifedine Kadry Arfat Ahmad Khan Giuseppe Espa Paola Feraco Jungeun Kim |
| author_sort | Deependra Rastogi |
| collection | DOAJ |
| description | Abstract The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients’ overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas. To ensure strong and reliable tumor segmentation, we employ 2D volumetric convolution neural network architectures that utilize a majority rule. This method helps to significantly decrease model bias and improve performance. Additionally, in order to predict survival rates, we extract radiomic features from the tumor regions that have been segmented, and then use a Deep Learning Inspired 3D replicator neural network to identify the most effective features. The model presented in this study was successful in segmenting brain tumors and predicting the outcome of enhancing tumor and real enhancing tumor. The model was evaluated using the BRATS2020 benchmarks dataset, and the obtained results are quite satisfactory and promising. |
| format | Article |
| id | doaj-art-dbf507e0ef7f4b7ca3a5728d2d8483b0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dbf507e0ef7f4b7ca3a5728d2d8483b02025-01-12T12:18:47ZengNature PortfolioScientific Reports2045-23222025-01-0115112710.1038/s41598-024-84386-0Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networksDeependra Rastogi0Prashant Johri1Massimo Donelli2Seifedine Kadry3Arfat Ahmad Khan4Giuseppe Espa5Paola Feraco6Jungeun Kim7School of Computer Science and Engineering, IILM UniversitySCSE, Galgotias UniversityDepartment of Civil, Environmental, Mechanical Engineering University of TrentoDepartment of Computer Science and Mathematics, Lebanese American UniversityDepartment of Engineering, Simpson UniversityRadiomics Laboratory, Department of Economy and Management, University of TrentoNeuroradiology Unit, Santa Chiara HospitalDepartment of Computer Engineering, Inha UniversityAbstract The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients’ overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas. To ensure strong and reliable tumor segmentation, we employ 2D volumetric convolution neural network architectures that utilize a majority rule. This method helps to significantly decrease model bias and improve performance. Additionally, in order to predict survival rates, we extract radiomic features from the tumor regions that have been segmented, and then use a Deep Learning Inspired 3D replicator neural network to identify the most effective features. The model presented in this study was successful in segmenting brain tumors and predicting the outcome of enhancing tumor and real enhancing tumor. The model was evaluated using the BRATS2020 benchmarks dataset, and the obtained results are quite satisfactory and promising.https://doi.org/10.1038/s41598-024-84386-0Brain tumorMagnetic resonance imagingFeature extractionSegmentationSurvival days predictionDeep learning |
| spellingShingle | Deependra Rastogi Prashant Johri Massimo Donelli Seifedine Kadry Arfat Ahmad Khan Giuseppe Espa Paola Feraco Jungeun Kim Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks Scientific Reports Brain tumor Magnetic resonance imaging Feature extraction Segmentation Survival days prediction Deep learning |
| title | Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks |
| title_full | Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks |
| title_fullStr | Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks |
| title_full_unstemmed | Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks |
| title_short | Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks |
| title_sort | deep learning integrated mri brain tumor analysis feature extraction segmentation and survival prediction using replicator and volumetric networks |
| topic | Brain tumor Magnetic resonance imaging Feature extraction Segmentation Survival days prediction Deep learning |
| url | https://doi.org/10.1038/s41598-024-84386-0 |
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