Manual three-dimensional reconstruction of patient specific lung anatomy from CT scans

Precise knowledge of bronchovascular anatomy is critical for lung surgery. In this video tutorial, we present a manual, semi-automatic segmentation workflow using open-source image processing software to create detailed three-dimensional models of the lungs from computed tomography scans. Structures segmented include the bronchus, lung lobes, pulmonary arteries and veins, and tumours. In this method, we have used various segmentation techniques including thresholding, painting, logical operations and smoothing to perform anatomical labelling. By following this workflow, segmentation times range from 1.5 to 2 hours per patient, which can be affected by both computed tomography image quality and contrast enhancement. Although manual segmentation is time-consuming, it offers a cost-effective alternative to commercial three-dimensional reconstruction software, particularly for educational and research applications.

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Publication Details

Journal
Multimedia Manual of Cardio-Thoracic Surgery
Published
2026-02-02
DOI
https://doi.org/10.1510/mmcts.2025.151
Primary Topic
Medical Image Segmentation Techniques
Type
article
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article

Manual three-dimensional reconstruction of patient specific lung anatomy from CT scans

Quinten J Mank, Jolanda Kluin, Sabrina Siregar, Alexander P W M Maat et al.
Multimedia Manual of Cardio-Thoracic Surgery
Medical Image Segmentation Techniques
article

Manual three-dimensional reconstruction of patient specific lung anatomy from CT scans

Quinten J Mank, Jolanda Kluin, Sabrina Siregar, Alexander P W M Maat, Amir H Sadeghi
article en

Abstract

Precise knowledge of bronchovascular anatomy is critical for lung surgery. In this video tutorial, we present a manual, semi-automatic segmentation workflow using open-source image processing software to create detailed three-dimensional models of the lungs from computed tomography scans. Structures segmented include the bronchus, lung lobes, pulmonary arteries and veins, and tumours. In this method, we have used various segmentation techniques including thresholding, painting, logical operations and smoothing to perform anatomical labelling. By following this workflow, segmentation times range from 1.5 to 2 hours per patient, which can be affected by both computed tomography image quality and contrast enhancement. Although manual segmentation is time-consuming, it offers a cost-effective alternative to commercial three-dimensional reconstruction software, particularly for educational and research applications.

Multimedia Manual of Cardio-Thoracic SurgeryVol. 2026
Radboud University Nijmegen (NL), Erasmus MC (NL), Radboud University Medical Center (NL), University Medical Center Utrecht (NL), Erasmus University Rotterdam (NL)
Quality Education
Openalex Percentile: Top 71%
Medical Image Segmentation Techniques
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