FPGA-based Hardware Software Co-design to Accelerate Brain Tumour Segmentation

Brain tumors are a major concern, being the leading cause of cancer-related deaths. Computer-aided diagnosis significantly reduces the workload on physicians and improves cancer diagnosis and treatment. Brain tumor segmentation is a computationally intensive image-processing task. In this paper, we propose an FPGA-based Hardware-Software Co-design to accelerate this task using Watershed and Otsu thresholding algorithms. The FPGA handles parallel components, while the CPU manages sequential tasks in the same System-on-Chip (SoC). Using PolarFire Icicle FPGA platform, we process 20 MRI brain scan images (128x128) from the Kaggle dataset. Implementing both algorithms in parallel on the FPGA results in a 1.97× acceleration compared to a CPU-only implementation, mainly achieved by a 1973× reduction in latency when moving the Otsu algorithm from the CPU to the FPGA. This optimization employs DSP/MATH blocks, loop unrolling, and pipelining techniques.