A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation

TL;DR

This paper introduces a novel SLCA-UNet architecture that enhances brain tumor segmentation accuracy in MRI images by integrating residual dense blocks and attention modules, demonstrating superior performance on the BraTS dataset.

Contribution

The paper presents a new SLCA-UNet model that improves upon traditional UNet by incorporating advanced modules for better feature extraction and accuracy in brain tumor segmentation.

Findings

Achieved Dice score of 0.845 on BraTS 2020 dataset

Attained high sensitivity and specificity metrics

Demonstrated improved segmentation performance over existing methods

Abstract

Brain tumor is deliberated as one of the severe health complications which lead to decrease in life expectancy of the individuals and is also considered as a prominent cause of mortality worldwide. Therefore, timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Biomedical image analysis is a widely known solution to diagnose brain tumor. Although MRI is the current standard method for imaging tumors, its clinical usefulness is constrained by the requirement of manual segmentation which is time-consuming. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools and the UNet architecture is commonly used for segmentation. However, the traditional UNet has limitations in terms of complexity, training, accuracy, and contextual information processing. As a result, the modified UNet architecture, which incorporates residual dense blocks, layered attention, and channel attention modules, in addition to stacked convolution, can effectively capture both coarse and fine feature information. The proposed SLCA UNet approach achieves good performance on the freely accessible Brain Tumor Segmentation (BraTS) dataset, with an average performance of 0.845, 0.845, 0.999, and 8.1 in terms of Dice, Sensitivity, Specificity, and Hausdorff95 for BraTS 2020 dataset, respectively.