| Summary: | Brain extraction is an important preprocessing step in Computer Aided Diagnosis (CAD) from brain MRI. It facilitates stripping off irrelevant extra-cranial tissues including skull, eyes, and neck muscles, thereby enhancing accuracy of inference of an AI CAD system. Numerous studies have been conducted to achieve this task ranging from old-school image processing techniques to more sophisticated deep learning approaches. This research investigates the performance of two deep learning-based semantic segmentation techniques, DeepLabV3+ and U-Net for brain extraction of brain MRI scanning images. The two networks were trained using the largest human brain MRI images dataset having more than 160 thousand training images and the performance is compared with SynthStrip from MIT, as the current state of the art system. Besides, the trained networks were also tested using newly collected MRI images from a private hospital, AIH, Islamabad. The result indicates that DeepLabV3+ outperforms SynthStrip and U-Net in all public datasets and produces comparable results from the new dataset with a mean Dice score of 0.98. Using the trained DeepLabV3+, an app called NIVE is developed for public use. Since the DeepLabV3+ was trained using the most comprehensive human brain MRI dataset to date, NIVE is essentially the most versatile brain extraction app capable of handling MRI images in all types of file formats, sequences, orientations, acquisition hardware, and subject age. © 2025, Semarak Ilmu Publishing. All rights reserved.
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