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

Ádám István Szűcs, Béla Kári, Oszkár Pártos

TL;DR

This work tackles automatic left-ventricle segmentation in SPECT MPI under very small labeled datasets and varied acquisition geometries. It proposes a three-part approach: a 3D U-Net trained with self-supervised learning and heavy augmentation, a Continuous Max-Flow refinement to enforce global segmentation consistency, and kernel-density-based shape priors to accommodate non-Gaussian LV shapes, especially in hypoperfused cases. The integrated CMF-shape-prior framework yields 5–10% improvements over prior state-of-the-art methods on complete FOV volumes, including ill-conditioned cases, while highlighting wall-thickness artefacts as an area for further refinement. The approach is implemented in PyTorch with standard DL and optimization tools and validated on 102 patient datasets across multiple acquisition settings, demonstrating practical potential for diagnostic centers with limited labeled data.

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.

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

TL;DR

This work tackles automatic left-ventricle segmentation in SPECT MPI under very small labeled datasets and varied acquisition geometries. It proposes a three-part approach: a 3D U-Net trained with self-supervised learning and heavy augmentation, a Continuous Max-Flow refinement to enforce global segmentation consistency, and kernel-density-based shape priors to accommodate non-Gaussian LV shapes, especially in hypoperfused cases. The integrated CMF-shape-prior framework yields 5–10% improvements over prior state-of-the-art methods on complete FOV volumes, including ill-conditioned cases, while highlighting wall-thickness artefacts as an area for further refinement. The approach is implemented in PyTorch with standard DL and optimization tools and validated on 102 patient datasets across multiple acquisition settings, demonstrating practical potential for diagnostic centers with limited labeled data.

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.
Paper Structure (15 sections, 3 equations, 1 figure, 1 table)

This paper contains 15 sections, 3 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. 3D U-Net SSL with augmentation
  4. Continuous Max-Flow enhancement
  5. Shape prior incorporation with density estimation
  6. Materials
  7. Data
  8. Acquisition
  9. Results
  10. Quantitative evaluation
  11. Implementation.
  12. Experiments
  13. Conclusion
  14. Compliance with ethical standards
  15. Acknowledgments

Figures (1)

  • Figure 1: Enhancement of the 3D SSL U-Net adam2023 approach with CMF Shape prior segmentation. The first column depicts the SPECT volume of the cardiac area, the second column shows the segmentation with 3D SSL U-Net, and the third column shows the results with CMF shape prior enhancement. Two patients with different cardiac conditions, \ref{['fig:pat_tc99m_stable_perfusion_defect']} with stable perfusion defect and \ref{['fig:pat_tc99_inferior_pref_defect']} patient showing signs of inferior perfusion defect.