Segmentation of MRI Image Using Patch-wise Approach
Keywords:
MRI, Convolutional neural network, Multi-modal, Patch-wiseAbstract
Multi-modal imaging integration is necessary for developing robust models for diseases and improving the statistical potency of already-developed imaging biomarkers. In computer vision, dense connections are frequently employed to enhance gradient flow and provide inherent deep supervision throughout training. In particular, DenseNet, which creates feed-forward direct linkages between every layer, has demonstrated remarkable performance in tasks related to the classification of natural images. The paper introduces a methodology Patch-wise approach to perform the segmentation of MRI images into distinct regions, namely Gray matter (GM), White matter (WM), and Cerebrospinal fluid CSF). The findings of our tests carried out with the help of the suggested system, show that the average Dice Similarity Coefficients for CSF: 0.965, GM: 0.935, and WM: 0.918 are notably high.
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