A Robust U-Net-Based Approach for Accurate Brain Tumor Segmentation Using Multimodal MRI Data

Abstract

Detecting and quantifying the extent of brain tumors poses a formidable challenge in medical centers. Magnetic Resonance Imaging (MRI) has developed as a non-invasive brain cancers' primary diagnostic tool, offering the crucial advantage of avoiding ionizing radiation. Brain tumor manually segmented boundaries within 3D MRI volumes is an exceedingly time-intensive task, heavily reliant on operator expertise. Among brain tumors, gliomas stand out as the prevalent and highly malignant, significantly impacting patients' life expectancy, particularly at their highest grade. Recognizing the pressing need for a reliable, completely automatic segmentation technique to efficiently assess tumor extent, this study introduces a robust approach. A completely automated brain tumor segmentation method is proposed, leveraging U-Net-based deep convolutional networks. This approach underwent rigorous evaluation on the Multimodal Brain Tumor Image Segmentation BraTS-19 dataset a widely recognized medical image analysis dataset featuring multimodal MRI scans of brain tumors, including glioblastoma, anaplastic astrocytoma, and lower-grade glioma, coupled with corresponding manual tumor segmentations. This dataset serves as a pivotal resource for advancing automatic brain tumor segmentation techniques and assessing their performance using metrics like the Dice score, which achieved 92% for entire tumor. Cross-validation results affirm the efficiency and promise of our method in achieving accurate segmentation.