Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventricles
This work addresses the challenge of reliable and precise left ventricle segmentation for ischemia detection in high-risk patients, offering a more accessible alternative to expensive commercial solutions, though it is incremental as it builds on existing self-supervised learning methods.
The paper tackled the problem of automatically segmenting left ventricles in SPECT images with small, low-quality datasets by combining Continuous Max-Flow with shape priors to augment a 3D U-Net self-supervised learning approach, resulting in a 5-10% increase in quantitative metrics over previous state-of-the-art solutions.
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.