A Study on the Use of Edge TPUs for Eye Fundus Image Segmentation

TL;DR

This study evaluates the performance of Edge TPU hardware acceleration on single-board computers for rapid fundus image segmentation, demonstrating sub-25 millisecond prediction times and potential for real-time medical imaging applications.

Contribution

It provides the first comprehensive analysis of SBCs with Edge TPU acceleration for medical image segmentation, including new generalization to ultrasound thyroid images.

Findings

Edge TPUs enable sub-25 ms prediction times on SBCs.

Hardware acceleration improves segmentation speed significantly.

Results applicable to other medical imaging segmentation tasks.

Abstract

Medical image segmentation can be implemented using Deep Learning methods with fast and efficient segmentation networks. Single-board computers (SBCs) are difficult to use to train deep networks due to their memory and processing limitations. Specific hardware such as Google's Edge TPU makes them suitable for real time predictions using complex pre-trained networks. In this work, we study the performance of two SBCs, with and without hardware acceleration for fundus image segmentation, though the conclusions of this study can be applied to the segmentation by deep neural networks of other types of medical images. To test the benefits of hardware acceleration, we use networks and datasets from a previous published work and generalize them by testing with a dataset with ultrasound thyroid images. We measure prediction times in both SBCs and compare them with a cloud based TPU system. The results show the feasibility of Machine Learning accelerated SBCs for optic disc and cup segmentation obtaining times below 25 milliseconds per image using Edge TPUs.