Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method
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...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Switzerland), 2021-04, Vol.21 (8), p.2675 |
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| creator | Rim, Beanbonyka Lee, Sungjin Lee, Ahyoung Gil, Hyo-Wook Hong, Min |
| description | 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. |
| doi_str_mv | 10.3390/s21082675 |
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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.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s21082675</identifier><identifier>PMID: 33920219</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Abnormalities ; Accuracy ; Algorithms ; Annotations ; Cardiovascular disease ; chest CT scans ; Cluster Analysis ; Clustering ; Computed tomography ; Coronary artery disease ; Coronary vessels ; Deep learning ; Experiments ; Heart attacks ; Humans ; image processing ; Image Processing, Computer-Assisted ; K-Means clustering ; Magnetic resonance imaging ; mathematical morphology method ; Medical imaging ; Morphology ; Performance evaluation ; Semantics ; silhouette score ; Tomography, X-Ray Computed ; whole cardiac segmentation</subject><ispartof>Sensors (Basel, Switzerland), 2021-04, Vol.21 (8), p.2675</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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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. 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| subjects | Abnormalities Accuracy Algorithms Annotations Cardiovascular disease chest CT scans Cluster Analysis Clustering Computed tomography Coronary artery disease Coronary vessels Deep learning Experiments Heart attacks Humans image processing Image Processing, Computer-Assisted K-Means clustering Magnetic resonance imaging mathematical morphology method Medical imaging Morphology Performance evaluation Semantics silhouette score Tomography, X-Ray Computed whole cardiac segmentation |
| title | Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method |
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