ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2014
Efficient ribcage segmentation from CT scans using shape features.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- 3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review.Journal of digital imaging · 2018Pooled it
- Atlas-based rib-bone detection in chest X-rays.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2016Article
- Osteoblastic lesion screening with an advanced post-processing package enabling in-plane rib reading in CT-images.BMC medical imaging · 2016Article
- Computer-aided pulmonary image analysis in small animal models.Medical physics · 2015Article
- A generic approach to pathological lung segmentation.IEEE transactions on medical imaging · 2014Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Rib cage structure and morphology is important for anatomical analysis of chest CT scans. A fundamental challenge in rib cage extraction is varying intensity levels and connection with adjacent bone structures including shoulder blade and sternum. In this study, we present a fully automated 3-D algorithm to segment the rib cage by detection and separation of other bone structures. The proposed approach consists of four steps. First, all high-intensity bone structures are segmented. Second, multi-scale Hessian analysis is performed to capture plateness and vesselness information. Third, with the plate/vessel features, bone structures other than rib cage are detected. Last, the detected bones are separated from rib cage with iterative relative fuzzy connectedness method. The algorithm was evaluated using 400 human CT scans and 100 small animal images with various resolution. The results suggested that the percent accuracy of rib cage extraction is over 95% with the proposed algorithm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.