Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images
This work addresses efficient and simplified brain tumor segmentation for medical imaging applications, but it is incremental as it adapts an existing method.
The paper tackles brain tumor segmentation in MRI images by proposing a lightweight U-Net implementation that achieves real-time segmentation with a mean IoU of 89% on the BITE dataset, outperforming standard benchmarks.
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Net shows very promising results on BITE dataset and it achieves a mean intersection-over-union (IoU) of 89% while outperforming the standard benchmark algorithms. Additionally, this work demonstrates an effective use of the three perspective planes, instead of the original three-dimensional volumetric images, for simplified brain tumor segmentation.