Overview of the Whole Heart and Heart Chamber Segmentation Methods

Habijan, Marija; Babin, Danilo; Galić, Irena; Leventić, Hrvoje; Romić, Krešimir; Velicki, Lazar; Pižurica, Aleksandra

doi:10.1007/s13239-020-00494-8

Cited by 31 publications

(25 citation statements)

References 113 publications

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“…We can assume that spine pixels are in the last right cluster in Figure 3b. Therefore, the spine threshold τ is the maximum centroid of clusters C. Then, the spine binary mask S, S ⊂ [0, 1] 2D , is a result of a thresholding condition, which is defined as shown in Equation (8).…”

Section: Convex Hull Of Spine Maskmentioning

confidence: 99%

“…In modern medical imaging modalities, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound are used to assist in identifying abnormal findings in the human body for early assessment and diagnosis [4][5][6][7]. Recently, the non-gated and non-invasive chest CT has been used to provide potential support for investigative imaging tests to interpret cardiac function states [8][9][10]. More detailed characteristics of chest CT images are described in Appendix A.…”

Section: Introductionmentioning

confidence: 99%

“…Cardiac segmentation in chest CT images has played a key role in partitioning the whole chest CT image into a number of anatomically meaningful regions, known as regions of interest (ROIs). Typically, the anatomical ROIs for cardiac segmentation focus on the four chambers of the heart-the left ventricle (LV), right ventricle (RV), left atrium (LA) and right atrium (RA)-or the entire heart anatomy, including the four chambers, coronary arteries and descending thoracic aorta (DA) [8][9][10]. The manual process of cardiac segmentation can be time-consuming and labor intensive for radiologists.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this burden, fully automatic methods have been proposed by applying computer-aided technologies [1,7-10]. These techniques have been built based on earlier approaches [8] such as graph-based segmenting [11,12], mean-thresholding [13][14][15][16][17] and fuzzy clustering methods [18][19][20][21]. Later, the deep learning approach showed promising successful performance [7,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Labeling the ground truth for cardiac segmentation requires the extensive manual annotation of images by the radiologist, which can be time-consuming and labor intensive [7][8][9][10][22][23][24][25][26]. Due to the difficulty in obtaining annotated data and the required expertise as an annotator, an unsupervised approach has been considered in this study.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method

Lee

Gil

et al. 2021

Sensors

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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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Section: Convex Hull Of Spine Maskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method

Lee

Gil

et al. 2021

Sensors

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Segmentation and Quantification of Bi-Ventricles and Myocardium Using 3D SERes-U-Net

Habijan

Galić

Leventić

et al. 2022

Communications in Computer and Information Science

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Automatic cardiac MRI segmentation, including left and right ventricular endocardium and epicardium, has an essential role in clinical diagnosis by providing crucial information about cardiac function. Determining heart chamber properties, such as volume or ejection fraction, directly relies on their accurate segmentation. In this work, we propose a new automatic method for the segmentation of myocardium, left, and right ventricles from MRI images. We introduce a new architecture that incorporates SERes blocks into 3D U-net architecture (3D SERes-U-Net). The SERes blocks incorporate squeeze-and-excitation operations into residual learning. The adaptive feature recalibration ability of squeeze-and-excitation operations boosts the network's representational power while feature reuse utilizes effective learning of the features, which improves segmentation performance. We evaluate the proposed method on the testing dataset of the MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset and obtain highly comparable results to the state-of-the-art methods.

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Slice-Mask Based 3D Cardiac Shape Reconstruction from CT Volume

Yuan

Liu

et al. 2023

Computer Vision – ACCV 2022

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Overview of the Whole Heart and Heart Chamber Segmentation Methods

Cited by 31 publications

References 113 publications

Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method

Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method

Segmentation and Quantification of Bi-Ventricles and Myocardium Using 3D SERes-U-Net

Slice-Mask Based 3D Cardiac Shape Reconstruction from CT Volume

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