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Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN
- Source :
- International Journal of Cognitive Informatics and Natural Intelligence; January 2022, Vol. 15 Issue: 4 p1-17, 17p
- Publication Year :
- 2022
-
Abstract
- We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three Dimensional Convolutional Neural Network (3D MedImg-CNN) approach which achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D MedImg-CNN is formed directly on the raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascaded architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our model also allows us to segment a complete cerebral image in one minute. The performance of the proposed 3D MedImg-CNN with CNN segmentation method is computed using dice similarity coefficient (DSC). In experiments on the 2013, 2015 and 2017 BraTS challenges datasets; we unveil that our approach is among the most powerful methods in the literature, while also being very effective.
Details
- Language :
- English
- ISSN :
- 15573958 and 15573966
- Volume :
- 15
- Issue :
- 4
- Database :
- Supplemental Index
- Journal :
- International Journal of Cognitive Informatics and Natural Intelligence
- Publication Type :
- Periodical
- Accession number :
- ejs59835300
- Full Text :
- https://doi.org/10.4018/IJCINI.20211001.oa4