Robust cardiac segmentation corrected with heuristics.

Cervantes-Guzmán, Alan; McPherson, Kyle; Olveres, Jimena; Moreno-García, Carlos Francisco; Robles, Fabián Torres; Elyan, Eyad; Escalante-Ramírez, Boris

Abstract

Cardiovascular diseases related to the right side of the heart, such as Pulmonary Hypertension, are some of the leading causes of death among the Mexican (and worldwide) population. To avoid invasive techniques such as catheterizing the heart, improving the segmenting performance of medical echocardiographic systems can be an option to early detect diseases related to the right-side of the heart. While current medical imaging systems perform well segmenting automatically the left side of the heart, they typically struggle segmenting the right-side cavities. This paper presents a robust cardiac segmentation algorithm based on the popular U-NET architecture capable of accurately segmenting the four cavities with a reduced training dataset. Moreover, we propose two additional steps to improve the quality of the results in our machine learning model, 1) a segmentation algorithm capable of accurately detecting cone shapes (as it has been trained and refined with multiple data sources) and 2) a post-processing step which refines the shape and contours of the segmentation based on heuristics provided by the clinicians. Our results demonstrate that the proposed techniques achieve segmentation accuracy comparable to state-of-the-art methods in datasets commonly used for this practice, as well as in datasets compiled by our medical team. Furthermore, we tested the validity of the post-processing correction step within the same sequence of images and demonstrated its consistency with manual segmentations performed by clinicians.

Citation

CERVANTES-GUZMÁN, A., MCPHERSON, K., OLVERES, J., MORENO-GARCÍA, C.F., ROBLES, F.T., ELYAN, E. and ESCALANTE-RAMÍREZ, B. 2023. Robust cardiac segmentation corrected with heuristics. PLoS ONE [online], 18(10), article e0293560. https://doi.org/10.1371/journal.pone.0293560

• Journal Article Type: Article
• Acceptance Date: Oct 15, 2023
• Online Publication Date: Oct 27, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Oct 29, 2023
• Publicly Available Date: Nov 13, 2023
• Journal: PLoS ONE
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 10
• Article Number: e0293560
• DOI: https://doi.org/10.1371/journal.pone.0293560
• Keywords: Cardiovascular diseases; Mexican population; Causes of death; Cardiac segmentation algorithm
• Public URL: https://rgu-repository.worktribe.com/output/2125454