ArticleCardiovascular engineering and technology2024
Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.
Article in Cardiovascular engineering and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Mechanical-medical convergence in heart failure: Artificial intelligence, finite-element modeling, and 3D printing for diagnosis and prognosis.Bioengineering & translational medicine · 2026Review
- Deep learning for imaging diagnosis of jaw cystic lesions and maxillofacial tumors: A narrative review.The Journal of international medical research · 2025Review
- A Low-Cost Workflow to Generate Virtual and Physical Three-Dimensional Models of Cardiac Structures.World journal for pediatric & congenital heart surgery · 2025Article
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Authors and funding
4 authors.
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Abstract
purposeCardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis.
methodThis work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest.
resultsThe algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29 mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots.
conclusionThe heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation.
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