Abstract:
Medical image segmentation is a critical task in medical image analysis and
patient diagnosis. This thesis investigates the application of the 3D U-Net
architecture and its variations, EquiUnet and Att_EquiUnet, for brain tumor
segmentation on the BraTS 2020 dataset. A comprehensive evaluation framework
was utilized to assess segmentation performance across whole tumor (WT), tumor
core (TC), and enhancing tumor (ET) regions. Results demonstrated the robust
performance of the baseline 3D U-Net, achieving high accuracy (91.19%) across all
tumor regions. EquiUnet did not exhibit significant performance gains over the
baseline U-Net. However, Att_EquiUnet, using the CBAM attention module,
showed improvements in boundary localization as evidenced by reduced Hausdorff
distances. The study also explored the impact of quantization on model size and
accuracy. 16-bit quantization emerged as an optimal compromise, achieving a
significant reduction in model size (to 25% of the original) while maintaining
accuracy and even slightly improving sensitivity in some cases. 8-bit quantization,
while further reducing model size (to 6.4%), incurred a more pronounced accuracy
loss, raising concerns about its suitability for clinical use. This thesis contributes to
the field by offering a comparative study of U-Net variants for 3D image
segmentation and highlighting the potential of attention mechanisms and 16-bit
quantization for improving model performance and clinical applicability.
