Whole heart CNN-based segmentation for phenotypical analysis of APOE mouse models using photon counting cine cardiac micro-CT data

A. J. Allphin; R. Nadkarni; Z. Han; D. P. Clark; A. Badea; C. T. Badea

doi:10.1117/12.3006853

2 April 2024 Whole heart CNN-based segmentation for phenotypical analysis of APOE mouse models using photon counting cine cardiac micro-CT data

A. J. Allphin, R. Nadkarni, Z. Han, D. P. Clark, A. Badea, C. T. Badea

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 129300G (2024) https://doi.org/10.1117/12.3006853
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

This paper presents a deep learning approach to automated segmentation of cardiac structures in 5D (3D + Time + Energy) Photon-Counting micro-CT (PCCT) imaging sets. We have acquired, reconstructed, and fully segmented a preclinical dataset of cardiac micro-PCCT scans in APOE mouse models. These APOE genotypes serve as models of varying degrees of risk of Alzheimer’s disease and cardiovascular disease. The dataset of user-guided segmentations served as the training data for a deep learning 3D UNet model capable of segmenting the four primary cardiac chambers, the aorta, pulmonary artery, inferior and superior vena cava, myocardium, and the pulmonary tree. Experimental results demonstrate the effectiveness of the proposed methodology in achieving reliable and efficient cardiac segmentation. We demonstrate the difference in performance when using single-energy PCCT images versus decomposed iodine maps as input. We achieved an average Dice score of 0.799 for the network trained on single-energy images and 0.756 for the network trained using iodine maps. User-guided segmentations took approximately 45 minutes/mouse while CNN segmentation took less than one second on a system with a single RTX 5000 GPU. This novel deep learning-based cardiac segmentation approach holds significant promise for advancing phenotypical analysis in mouse models of cardiovascular disease, offering a reliable and time-efficient solution for researchers working with photon-counting micro-CT imaging data.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

A. J. Allphin, R. Nadkarni, Z. Han, D. P. Clark, A. Badea, and C. T. Badea "Whole heart CNN-based segmentation for phenotypical analysis of APOE mouse models using photon counting cine cardiac micro-CT data", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129300G (2 April 2024); https://doi.org/10.1117/12.3006853

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