3D Segmentation of Whole Lung and Metastatic Lung Nodules Using Adaptive Region Growing and Shape-based Morphology
The proposed semiautomated CAD system, RGLNS, requiring minimal manual input, demonstrated robust, and promising segmentation results for the whole lung and various types of metastatic lung nodules.
Authors
- Sameer Bakhshi (ORCID: https://orcid.org/0000-0001-9367-4407)
- Shuvadeep Ganguly (ORCID: https://orcid.org/0000-0002-7296-6088)
- Amit Mehndiratta (ORCID: https://orcid.org/0000-0001-6477-2462)
- Esha Baidya Kayal (ORCID: https://orcid.org/0000-0002-0377-826X)
- Krithika Rangarajan (ORCID: https://orcid.org/0000-0001-5376-6390)
- Manish Kumar Saini (ORCID: https://orcid.org/0000-0003-1564-5058)
- Devasenathipathy Kandasamy (ORCID: https://orcid.org/0000-0002-8615-4937)
- Swetambri Sharma
- Nikhil Sharma (ORCID: https://orcid.org/0000-0001-8903-2938)
- Shivansh Gupta (ORCID: https://orcid.org/0009-0002-0203-0206)
- D. S. Dheeksha
- Archana Sasi (ORCID: https://orcid.org/0000-0002-7095-6740)
Institutions
- All India Institute of Medical Sciences (IN)
- Indian Institute of Technology Delhi (IN)
Publication Details
- Journal
- Journal of Computer Assisted Tomography
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1097/rct.0000000000001719
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00