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Segmentation and Volumetric Analysis of Heart from Cardiac CT Images

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Abstract

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.

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Data Availability

This work did not use any primary data (images) during the research. All the images were considered from the published work (cited).

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Acknowledgements

We would like to acknowledge Mr. Yogeesh M, the project manager at SIEMENS Healthcare, Germany for his valuable guidance to realize this requirement from the technical perspective (code reviews and suggestions for code optimization).

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India

    Rashmitha & K. N. Manjunath

  2. Consultant in Radiation Oncology, Clinical Informatics and Artificial Intelligence, Karkinos Healthcare, Bengaluru, India

    Anjali Kulkarni

  3. Healthcare IT, Softrams, 205 161 Fort Evans Rd, Leesburg, VA, 20176, USA

    Vamshikrishna Kulkarni

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  1. Rashmitha
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Contributions

R: Methodology, Writing original draft, Work design, Coding, results interpretation, getting feedback from the doctor. KNM: Conceptualization, Checking the data authenticity and its diagnostic quality, Investigation, Methodology, Visualization, Validation, Writing original draft. AK: Conceptualization, Data curation, Formal analysis, Project administration, Supervision, Validation, Checking the data authenticity and its diagnostic quality. VK: Investigation, Formal analysis, Computing resources, Code review, Review and editing.

Corresponding author

Correspondence to K. N. Manjunath.

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Competing interest

The authors have no relevant financial or non-financial interests to disclose

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The authors have no conflict of interest in this work

Research Involving Human Participants and Animals

This study did not involve any human participants either directly or indirectly

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Not applicable

Additional information

Associate Editor Derek J. Dosdall, Ph.D. oversaw the review of this article.

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Rashmitha, Manjunath, K.N., Kulkarni, A. et al. Segmentation and Volumetric Analysis of Heart from Cardiac CT Images. Cardiovasc Eng Tech (2024). https://doi.org/10.1007/s13239-024-00715-4

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  • DOI: https://doi.org/10.1007/s13239-024-00715-4

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