Mouse Arterial Wall Imaging and Analysis From Synchrotron X-Ray Microtomography

Ubiquitous accumulation of large volumes of data, and in- creased availability of annotated medical data in particular, has made it possible to show the many and varied benefits of deep learning to the semantic segmentation of medical im- ages. Nevertheless, data access and annotation come at a high cost in clinician time. The power of Vision Transformer (ViT) is well-documented for generic computer vision tasks involv- ing millions of images of every day objects, of which only relatively few have been annotated. Its translation to rela- tively more modest (i.e. thousands of images of) medical data is not immediately straightforward. This paper presents prac- tical avenues for training a Computationally-Efficient Semi- Supervised Vision Transformer (CESS-ViT) for medical im- age segmentation task. We propose a self-attention-based image segmentation network which requires only limited computational resources. Additionally, we develop a dual pseudo-label supervision scheme for use with semi-supervision in a simple pure ViT. Our method has been evaluated on a publicly available cardiac MRI dataset with direct comparison against other semi-supervised methods. Our results illustrate the proposed ViT-based semi-supervised method outperforms the existing methods in the semantic segmentation of cardiac ventricles.