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Segmentation and Volumetric Analysis of Heart from Cardiac CT Images
Rashmitha1, K N Manjunath2, Anjali Kulkarni3
1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Insights
This study introduces an automated method for segmenting the heart in cardiac CT scans, improving efficiency and accuracy for cardiovascular disease diagnosis. The developed algorithm accurately delineates heart structures without human intervention.
Area of Science:
- Medical Imaging
- Computer-Aided Diagnosis
- Cardiovascular Imaging
Background:
- Cardiac CT is crucial for cardiovascular disease evaluation.
- Accurate heart segmentation is vital for diagnosis and management but remains challenging.
- Manual segmentation is time-consuming, labor-intensive, and prone to errors.
Purpose of the Study:
- To develop a fast, fully automatic segmentation method for the heart from CT images.
- To overcome the limitations of manual segmentation in cardiac imaging.
- To enhance the efficiency and accuracy of cardiovascular image analysis.
Main Methods:
- An image-driven processing pipeline was developed.
- Techniques include adaptive median filter, multi-level thresholding, active contours, and mathematical morphology.
- Incorporation of human anatomy knowledge for region delineation.
Main Results:
- The automated method achieved an average Dice score of 0.866 and Jaccard score of 0.776.
- Average Hausdorff distance was 33.29 mm for segmenting heart chambers, aorta, and blood vessels.
- Results were validated against ground truths using Bland-Altman plots.
Conclusions:
- The proposed method successfully segmented the heart from CT images automatically.
- This technique can be a foundation for developing AI-based segmentation solutions.
- Automated segmentation offers improved efficiency and accuracy in cardiac image analysis.
Purpose:
Cardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis.
Method:
This work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest.
Results:
The algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29 mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots.
Conclusion:
The heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation.
