Siva Shankar S

Abstract For monitoring the progression of the disease and the efficacy of treatment, it is essential to segment the brain tumor. The majority of the available deep learning models in use today are based on discrete time points without considering the continuity of the process, which causes irregular shapes of the tumors in the subsequent images.

Abstract Proper monitoring of tumor progression and evaluation of treatment responses highly depend on longitudinal brain tumor segmentation from MRI data. Current deep learning methodologies have mainly concentrated on analyzing single-time-point images, which restricts the ability to incorporate temporal dynamics during the segmentation process. The study proposes a new approach called Temporal-Spatial Transformer Network (TST-Net), which can be used for longitudinal brain tumor segmentation.

Abstract Early and accurate identification of brain tumors from magnetic resonance imaging (MRI) is essential for timely clinical intervention; however, manual interpretation remains time-consuming and dependent on expert analysis.