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Reproducibility and Repeatability of Coronary Computed Tomography Angiography (CCTA) Image Segmentation in Detecting
Mardhiyati Mohd Yunus1,2, Akmal Sabarudin1, Muhammad Khalis Abdul Karim3
1Programme of Diagnostic Imaging and Radiotherapy, Faculty of Health Sciences, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia.
Insights
This study evaluated manual versus semi-automatic segmentation of coronary computed tomography angiography (CCTA) images for atherosclerosis. Semi-automatic segmentation is best for multiple observers, while manual segmentation is suitable for single-observer studies.
Area of Science:
- Radiology and Medical Imaging
- Cardiovascular Disease Research
- Quantitative Image Analysis
Background:
- Atherosclerosis is a leading cause of heart disease mortality in Malaysia.
- Coronary computed tomography angiography (CCTA) is standard for diagnosis but qualitative assessments of noncalcified lesions can be inconsistent.
- Quantitative radiomics analysis of CCTA images offers potential for more objective assessment.
Purpose of the Study:
- To assess the reproducibility and repeatability of manual and semi-automatic segmentation methods for atherosclerotic lesions in CCTA images.
- To determine the optimal CCTA image segmentation technique for quantitative radiomics analysis.
Main Methods:
- Retrospective analysis of 30 CCTA images from Hospital Canselor Tuanku Muhriz (HCTM).
- Manual and semi-automatic segmentation of three main coronary arteries using LIFEx software by two independent experts.
- Extraction of 11,700 radiomics features (first-order, second-order, shape) from 180 segmentations.
Main Results:
- Manual segmentation with soft-tissue windowing showed higher repeatability (intra-CC: 0.961 for first-order/shape, 0.924 for second-order).
- Semi-automatic segmentation demonstrated higher reproducibility (inter-CC: 0.920 for first-order, 0.839 for second-order).
- Both methods achieved excellent reproducibility and repeatability (intra-CC > 0.9) for all feature types.
Conclusions:
- Semi-automated segmentation is recommended for inter-observer studies due to higher reproducibility.
- Manual segmentation with soft-tissue windowing is suitable for single-observer studies, offering high repeatability.
- Quantitative radiomics analysis of CCTA images can be reliably performed using either segmentation method, depending on the study design.
Abstract:
Atherosclerosis is known as the leading factor in heart disease with the highest mortality rate among the Malaysian population. Usually, the gold standard for diagnosing atherosclerosis is by using the coronary computed tomography angiography (CCTA) technique to look for plaque within the coronary artery. However, qualitative diagnosis for noncalcified atherosclerosis is vulnerable to false-positive diagnoses, as well as inconsistent reporting between observers. In this study, we assess the reproducibility and repeatability of segmenting atherosclerotic lesions manually and semiautomatically in CCTA images to identify the most appropriate CCTA image segmentation method for radiomics analysis to quantitatively extract the atherosclerotic lesion. Thirty (30) CCTA images were taken retrospectively from the radiology image database of Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, Malaysia. We extract 11,700 radiomics features which include the first-order, second-order and shape features from 180 times of image segmentation. The interest vessels were segmentized manually and semiautomatically using LIFEx (Version 7.0.15, Institut Curie, Orsay, France) software by two independent radiology experts, focusing on three main coronary blood vessels. As a result, manual segmentation with a soft-tissuewindowing setting yielded higher repeatability as compared to semiautomatic segmentation with a significant intraclass correlation coefficient (intra-CC) 0.961 for thefirst-order and shape features; intra-CC of 0.924 for thesecond-order features with p < 0.001. Meanwhile, the semiautomatic segmentation has higher reproducibility as compared to manual segmentation with significant interclass correlation coefficient (inter-CC) of 0.920 (first-order features) and a good interclass correlation coefficient of 0.839 for the second-order features with p < 0.001. The first-order, shape order and second-order features for both manual and semiautomatic segmentation have an excellent percentage of reproducibility and repeatability (intra-CC > 0.9). In conclusion, semi-automated segmentation is recommended for inter-observer study while manual segmentation with soft tissue-windowing can be used for single observer study.
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