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Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusivetechnique for medical image acquisition. Brain tumor segmentation is theprocess of algorithmically identifying tumors in brain MRI scans. While manyapproaches have been proposed in the literature for brain tumor segmentation,this paper proposes a lightweight implementation of U-Net. Apart from providingreal-time segmentation of MRI scans, the proposed architecture does not needlarge amount of data to train the proposed lightweight U-Net. Moreover, noadditional data augmentation step is required. The lightweight U-Net shows verypromising results on BITE dataset and it achieves a meanintersection-over-union (IoU) of 89% while outperforming the standard benchmarkalgorithms. Additionally, this work demonstrates an effective use of the threeperspective planes, instead of the original three-dimensional volumetricimages, for simplified brain tumor segmentation.

Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images