Authors:
Gouranga Bala
;
Hiranmay Mondal
and
Amit Sethi
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
Keyword(s):
Brain MRI, Segmentation, U-Net, SegNet, Robust Loss Functions, Robust Dice Loss, Brain Tumor Segmentation, Medical Image Analysis.
Abstract:
This paper presents a comprehensive comparative study of brain tumor segmentation using two well-known Convolutional Neural Network (CNN) architectures, U-Net and SegNet, across multiple MRI modalities, specifically T2-weighted and Fluid Attenuated Inversion Recovery (FLAIR) images from the BraTS 2020 dataset. We evaluated the performance of these models using four different loss functions: Dice Loss, Focal Loss, Adaptive Robust Loss, and the novel Robust Dice Loss. Our contributions are twofold: first, we provide a detailed comparison of the performance of U-Net and SegNet for brain tumor segmentation across distinct MRI modalities, offering insights into the role of modality-specific features in segmentation outcomes. Second, we introduce the novel Robust Dice Loss, which significantly improved SegNet’s training efficiency, allowing it to handle challenging segmentation scenarios involving data imbalance and intricate tumor boundaries with much greater ease. Our results indicate th
at U-Net generally outperforms SegNet in terms of segmentation accuracy, particularly when trained with Adaptive Robust Loss. However, the introduction of Robust Dice Loss enabled SegNet to achieve competitive performance, particularly with the FLAIR modality, demonstrating its potential as an effective alternative. This study emphasizes the importance of selecting appropriate loss functions to handle imbalanced data and enhance model performance, thereby contributing valuable insights for the advancement of automated medical image analysis and its clinical utility in neuro-oncology.
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