High-precision brain tumor segmentation with switchable normalization in faster R-CNN architecture

Brain tumor segmentation from multi-modal MRI scans remains challenging due to the heterogeneity of tumors, intensity variations, and different protocols. In this study, a Switchable Normalization-based Faster R-CNN (SNFRC) framework is proposed. Furthermore, by employing the region proposal network (RPN), which uses switchable normalization (SN), heterogeneous data distributions can be tackled effectively to enhance feature consistency. A detection-based segmentation strategy is applied for the explicit localization of the tumor before creating the pixel-wise mask for accurate identification of irregular small tumor regions. Also, a composite loss function was introduced that adds the Dice loss, L2 loss, and Kullback-Leibler divergence losses for jointly optimising all the spatial overlaps, intensity reconstruction, and probabilistic regularisation. To evaluate the performance of the proposed architecture, the subject-wise splits of three benchmark datasets (BraTS 2018, 2019, and 2020) are used. The experimental results demonstrate that the proposed SNFRC outperforms CNN, DenseNet, and even the baseline Faster R-CNN consistently, with Dice scores above 93% and Hausdorff distance decreased by almost 1.5 pixels. Subsequent ablation and normalization experiments validate the efficacy of switchable normalization, yielding improvements of six to seven% over regular normalization. Given the training convergence analysis, this model optimizes faster and more stably than the baseline. The framework shows good performance over the dataset it was trained on, and it is computationally efficient enough that it can be used in real-time. However, achieving cross-dataset generalisation and conducting clinical validation are two important areas for future work. Overall, the SNFRC framework offers a reliable and efficient approach for the automated segmentation of brain tumors in multi-modal MRI.