CPU-based real time cardiac MRI segmentation using lightweight neural network and knowledge distillation

Motivation: Cardiac MRI plays an important role in diagnosis and prognosis of cardiovascular disease. Ideally, clinicians wants to get real-time segmentation using the existing CPU device but is challenging due to high computation burden of current neural networks. Goal(s): We aim to develop a lightweight network to accelerate cardiac MRI segmentation on a CPU device while maintaining the accuracy. Approach: We used layer-wish knowledge distillation to improve the accuracy of the lightweight network. Results: Our results showed that the accuracy of the lightweight model can satisfy the real-time segmentation on CPU devices and achieve the same level of accuracy as the complex model. Impact: This research provides a way to significantly reduce the running time of neural network on CPU device while maintaining accuracy using knowledge distillation. It facilitates the deployment of neural network in clinical practice by eliminating the need for additional hardware.