Efficient Implementation of AI Algorithms on an FPGA-based System for Enhancing Blood Vessel Segmentation

Abstract Machine learning offers the potential to enhance real-time image analysis in surgical operations. This paper presents results from the implementation of machine learning algorithms targeted for an intelligent image processing system comprising a custom CMOS image sensor and field programmable gate array (FPGA). A novel method is presented for efficient image segmentation and minimises energy usage and requires low memory resources, which makes it suitable for implementation. Using two eigenvalues of the enhanced Hessian image, simplified traditional machine learning and deep learning methods are employed to learn the prediction of blood vessels. Quantitative comparisons are provided between different machine learning models based on accuracy, resource utilisation, throughput, and power usage. It is shown how a gradient boosting decision tree with 1000 times fewer parameters can achieve comparable state-of-the-art performance whilst only using a much smaller proportion of the resources and producing a 200 MHz design that operates at 1,779 frames per second at 3.85 W, making it highly suitable for the proposed system. A methodology for implementing the AI algorithms onto FPGA is presented and then used to provide additional results by extending the original work to a 512 × 512 image size along with more detailed analysis.