Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points

Ted Shi; Maysam Shahedi; Kayla Caughlin; James D. Dormer; Ling Ma; Baowei Fei

doi:10.1117/12.2611594

4 April 2022 Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points

Ted Shi, Maysam Shahedi, Kayla Caughlin, James D. Dormer, Ling Ma, Baowei Fei

Author Affiliations +

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120341W (2022) https://doi.org/10.1117/12.2611594
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Given the prevalence of cardiovascular diseases (CVDs), the segmentation of the heart on cardiac computed tomography (CT) remains of great importance. Manual segmentation is time-consuming and intra-and inter-observer variabilities yield inconsistent and inaccurate results. Computer-assisted, and in particular, deep learning approaches to segmentation continue to potentially offer an accurate, efficient alternative to manual segmentation. However, fully automated methods for cardiac segmentation have yet to achieve accurate enough results to compete with expert segmentation. Thus, we focus on a semi-automated deep learning approach to cardiac segmentation that bridges the divide between a higher accuracy from manual segmentation and higher efficiency from fully automated methods. In this approach, we selected a fixed number of points along the surface of the cardiac region to mimic user interaction. Points-distance maps were then generated from these points selections, and a three-dimensional (3D) fully convolutional neural network (FCNN) was trained using points-distance maps to provide a segmentation prediction. Testing our method with different numbers of selected points, we achieved a Dice score from 0.742 to 0.917 across the four chambers. Specifically. Dice scores averaged 0.846 ± 0.059, 0.857 ± 0.052, 0.826 ± 0.062, and 0.824 ± 0.062 for the left atrium, left ventricle, right atrium, and right ventricle, respectively across all points selections. This point-guided, image-independent, deep learning segmentation approach illustrated a promising performance for chamber-by-chamber delineation of the heart in CT images.

Citation Download Citation

Ted Shi, Maysam Shahedi, Kayla Caughlin, James D. Dormer, Ling Ma, and Baowei Fei "Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120341W (4 April 2022); https://doi.org/10.1117/12.2611594

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