Abstract
Using deep neural networks for segmenting an MRI image of heterogeneously distributed pixels into a specific class assigning a label to each pixel is the concept of the proposed approach. This approach facilitates the application of the segmentation process on a preprocessed MRI image, with a trained network to be utilized for other test images. As labels are considered expensive assets in supervised training, fewer training images and training labels are used to obtain optimal accuracy. To validate the performance of the proposed approach, an experiment is conducted on other test images (available in the same database) that are not part of the training; the obtained result is of good visual quality in terms of segmentation and quite similar to the ground truth image. The average computed Dice similarity index for the test images is approximately 0.8, whereas the Jaccard similarity measure is approximately 0.6, which is better compared to other methods. This implies that the proposed method can be used to obtain reference images almost similar to the segmented ground truth images.
Highlights
Deep neural networks have been highly successful in segmenting outdoor scenes with high complexity, dissimilar patterns, variable texture, and wide pixel range
This model is used for segmenting MRI images of the brain, which are relatively simpler than outdoor scenes. e precise segmentation of a 2D image has always been a challenging task, and various approaches have been proposed for better accuracy, such as supervised and unsupervised, manual and automatic, and standalone and neural network-based techniques
Brain MRI segmentation is fundamental in several clinical applications and influences the outcome of the entire analysis because various processing operations rely on accurate segmentation of anatomical and structural regions
Summary
Deep neural networks have been highly successful in segmenting outdoor scenes with high complexity, dissimilar patterns, variable texture, and wide pixel range. This model is used for segmenting MRI images of the brain, which are relatively simpler than outdoor scenes. E precise segmentation of a 2D image has always been a challenging task, and various approaches have been proposed for better accuracy, such as supervised and unsupervised, manual and automatic, and standalone and neural network-based techniques. Deep convolutional neural networks (CNNs) have been effective in machine learning and have had impact on various industrial, medical, and commercial fields. Neural networks have been developed for medical image processing, in MRI image segmentation and Alzheimer’s disease classification [1, 2]. Deep neural network are proving to be better, highly computational for large data, and powerful because of encoderdecoder-based network or CNN architecture. We can get our segmentation result using our model. e only problem will be to train the network as it requires a large amount of ground truth and design the network appropriately
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