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Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multicenter, and
H A Kirişli1, M Schaap, S Klein
1Biomedical Imaging Group Rotterdam, Department of Radiology and Department of Medical Informatics, Erasmus MC, 3000 CA Rotterdam, The Netherlands. h.kirisli@erasmusmc.nl
Insights
This study presents an automatic method for segmenting cardiac chambers using computed tomography angiography (CTA) data. The approach accurately delineates heart structures, improving diagnostic capabilities for coronary artery disease (CAD).
Area of Science:
- Medical Imaging
- Cardiovascular Imaging
- Image Segmentation
Background:
- Computed tomography angiography (CTA) is vital for diagnosing coronary artery disease (CAD).
- Assessing ventricular and atrial function from CTA data can enhance diagnostic value.
- Accurate cardiac chamber delineation is crucial for extracting functional information like stroke volume and ejection fraction.
Purpose of the Study:
- To investigate the accuracy and robustness of a multiatlas-based segmentation method for cardiac chamber delineation.
- To evaluate the method's performance on multicenter and multivendor CTA data.
Main Methods:
- A fully automatic multiatlas-based segmentation method was developed.
- Eight atlas images were registered to patient CTA scans.
- Manual labels from atlases were propagated and combined using majority voting for segmentation.
Main Results:
- The method achieved a mean surface-to-surface error of 0.94 ± 1.12 mm and a Dice coefficient of 0.93 on 3D atlas data.
- On 2D multivendor data, mean error was 1.26 ± 1.25 mm and Dice coefficient was 0.91.
- Qualitative evaluation showed high accuracy, with 49% of images segmented below 1 mm error.
Conclusions:
- A fully automatic method for whole heart and cardiac chamber segmentation using CTA data was successfully developed and evaluated.
- The method demonstrated accuracy and robustness across multicenter and multivendor datasets.
- This technique holds promise for improving cardiac function assessment in clinical practice.
Purpose:
Computed tomography angiography (CTA) is increasingly used for the diagnosis of coronary artery disease (CAD). However, CTA is not commonly used for the assessment of ventricular and atrial function, although functional information extracted from CTA data is expected to improve the diagnostic value of the examination. In clinical practice, the extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction, requires accurate delineation of cardiac chambers. In this paper, we investigated the accuracy and robustness of cardiac chamber delineation using a multiatlas based segmentation method on multicenter and multivendor CTA data.
Methods:
A fully automatic multiatlas based method for segmenting the whole heart (i.e., the outer surface of the pericardium) and cardiac chambers from CTA data is presented and evaluated. In the segmentation approach, eight atlas images are registered to a new patient's CTA scan. The eight corresponding manually labeled images are then propagated and combined using a per voxel majority voting procedure, to obtain a cardiac segmentation.
Results:
The method was evaluated on a multicenter/multivendor database, consisting of (1) a set of 1380 Siemens scans from 795 patients and (2) a set of 60 multivendor scans (Siemens, Philips, and GE) from different patients, acquired in six different institutions worldwide. A leave-one-out 3D quantitative validation was carried out on the eight atlas images; we obtained a mean surface-to-surface error of 0.94 +/- 1.12 mm and an average Dice coefficient of 0.93 was achieved. A 2D quantitative evaluation was performed on the 60 multivendor data sets. Here, we observed a mean surface-to-surface error of 1.26 +/- 1.25 mm and an average Dice coefficient of 0.91 was achieved. In addition to this quantitative evaluation, a large-scale 2D and 3D qualitative evaluation was performed on 1380 and 140 images, respectively. Experts evaluated that 49% of the 1380 images were very accurately segmented (below 1 mm error) and that 29% were accurately segmented (error between 1 and 3 mm), which demonstrates the robustness of the presented method.
Conclusions:
A fully automatic method for whole heart and cardiac chamber segmentation was presented and evaluated using multicenter/multivendor CTA data. The accuracy and robustness of the method were demonstrated by successfully applying the method to 1420 multicenter/ multivendor data sets.
