Combining multi-view ensemble and surrogate lagrangian relaxation for real-time 3D biomedical image segmentation on the edge

Abstract

Real-time 3D biomedical image segmentation is always preferred considering the exponentially growing medical imaging data for the past decade. Recently deep learning has significantly boosted the performance of automatic medical image segmentation with high computation and memory requirements, especially for 3D biomedical images. Meanwhile, the privacy and security of patient data have always been the primary concern in medical applications among hospitals and clinics, and there also exists some applications which need real-time processing in clinic practice. Thus, 3D biomedical image segmentation is typically required to be performed locally (i.e. on the edge) with limited computation and memory resources. In this paper, we propose to combine multi-view ensemble and Surrogate Lagrangian relaxation (SLR) for real-time 3D biomedical image segmentation on the edge. Instead of directly dealing with 3D biomedical images, our segmentation conducts on the three 2D domains of the 3D images with an ensemble strategy. In addition, Surrogate Lagrangian relaxation is proposed to compress the model to enable high efficiency and real-time processing. Experiments on a typical edge Nvidia GPU show that our method achieves real-time processing which is 1.5× faster with an improvement of 9% on accuracy compared with single-view models. It also saves 26× computational resources and 6× memory resources compared to 3D segmentation models.