Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventricles

TL;DR

This paper introduces a novel method combining continuous max-flow with shape priors to enhance self-supervised 3D U-Net segmentation of the left ventricle in low-quality SPECT images, improving accuracy in few-shot settings.

Contribution

It proposes a new augmentation technique using continuous max-flow and shape priors to improve self-supervised learning for SPECT cardiac segmentation.

Findings

Achieved 5-10% improvement over state-of-the-art methods.

Effective in high-noise, low-quality SPECT datasets.

Demonstrated robustness across different SPECT geometries.

Abstract

Single-Photon Emission Computed Tomography (SPECT) left ventricular assessment protocols are important for detecting ischemia in high-risk patients. To quantitatively measure myocardial function, clinicians depend on commercially available solutions to segment and reorient the left ventricle (LV) for evaluation. Based on large normal datasets, the segmentation performance and the high price of these solutions can hinder the availability of reliable and precise localization of the LV delineation. To overcome the aforementioned shortcomings this paper aims to give a recipe for diagnostic centers as well as for clinics to automatically segment the myocardium based on small and low-quality labels on reconstructed SPECT, complete field-of-view (FOV) volumes. A combination of Continuous Max-Flow (CMF) with prior shape information is developed to augment the 3D U-Net self-supervised learning (SSL) approach on various geometries of SPECT apparatus. Experimental results on the acquired dataset have shown a 5-10\% increase in quantitative metrics based on the previous State-of-the-Art (SOTA) solutions, suggesting a good plausible way to tackle the few-shot SSL problem on high-noise SPECT cardiac datasets.