Open Access3DMSNET: 3D CNN Based Brain MRI Segmentation
Author(s) -
Jamal Ahmad,
Bhanu Bhaskar Kotagiri,
Haresh Seetharaman,
Ajay Kumar,
J. Arunnehru
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e1027.0785s319
Subject(s) - segmentation , computer science , artificial intelligence , pattern recognition (psychology) , market segmentation , similarity (geometry) , task (project management) , scale space segmentation , image segmentation , computer vision , image (mathematics) , management , marketing , economics , business
Segmentation of the brain images has become an important task to analyze the abnormality in infants. Automatic methods are important as the infant brain growth has to be tracked and it is almost impossible for an individual to manually segment the MRI data on particular intervals. The manual segmentation tasks are time-consuming and require highly skilled professionals to segment images. Automatic segmentation methods have gained huge support for segmenting MRI images. Several segmentation methods lack accuracies due to nearest neighbor or self-similarity problems. The CNNs have outperformed the traditional methods and are proving to be more reliable day by day. The proposed method is a patch-based method which uses 3DMSnet (3D Multi-Scale Network) for segmentation. The model is evaluated on BrainWeb and other publicly available datasets.