ArticleSensors (Basel, Switzerland)2021
Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.Cardiovascular engineering and technology · 2024Article
- An automated segmentation of coronary artery calcification using deep learning in specific region limitation.Medical & biological engineering & computing · 2023Article
- Introduction of Materials Genome Technology and Its Applications in the Field of Biomedical Materials.Materials (Basel, Switzerland) · 2023Review
- Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images.PeerJ. Computer science · 2022Article
- Deep-Learning-Based Coronary Artery Calcium Detection from CT Image.Sensors (Basel, Switzerland) · 2021Article
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5 authors.
Funding
Abstract
Whole cardiac segmentation in chest CT images is important to identify functional abnormalities that occur in cardiovascular diseases, such as coronary artery disease (CAD) detection. However, manual efforts are time-consuming and labor intensive. Additionally, labeling the ground truth for cardiac segmentation requires the extensive manual annotation of images by the radiologist. Due to the difficulty in obtaining the annotated data and the required expertise as an annotator, an unsupervised approach is proposed. In this paper, we introduce a semantic whole-heart segmentation combining K-Means clustering as a threshold criterion of the mean-thresholding method and mathematical morphology method as a threshold shifting enhancer. The experiment was conducted on 500 subjects in two cases: (1) 56 slices per volume containing full heart scans, and (2) 30 slices per volume containing about half of the top of heart scans before the liver appears. In both cases, the results showed an average silhouette score of the K-Means method of 0.4130. Additionally, the experiment on 56 slices per volume achieved an overall accuracy (OA) and mean intersection over union (mIoU) of 34.90% and 41.26%, respectively, while the performance for the first 30 slices per volume achieved an OA and mIoU of 55.10% and 71.46%, respectively.
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