Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation
This work addresses the problem of inconsistent MRI intensities across institutes for medical researchers and clinicians, but it is incremental as it builds on existing transformer and adversarial training methods.
The paper tackles brain tumor segmentation from multi-modal MRI volumes by proposing a volumetric vision transformer with hybrid window attention and local distributional smoothness training, achieving Dice scores of 81.71%, 91.38%, and 85.40% for enhancing tumor, whole tumor, and tumor core, respectively.
As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors. In this concept, we propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features and local distributional smoothness (LDS) during model training inspired by virtual adversarial training (VAT) to make the model robust. We trained and evaluated network architecture on the FeTS Challenge 2022 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.71%, 91.38% and 85.40%; Hausdorff Distance (95%) of 14.81 mm, 3.93 mm, 11.18 mm for the enhancing tumor, whole tumor, and tumor core, respectively. Overall, the experimental results verify our method's effectiveness by yielding better performance in segmentation accuracy for each tumor sub-region. Our code implementation is publicly available : https://github.com/himashi92/vizviva_fets_2022