Method for segmentation of knee articular cartilages based on contrast-enhanced CT images
|Author:||Myller, Katariina A. H.1,2; Honkanen, Juuso T. J.1,2,3; Jurvelin, Jukka S.1;|
1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
2Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
3Center of Oncology, Kuopio University Hospital, Kuopio, Finland
4Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
5Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
6Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019111438084
|Publish Date:|| 2019-11-14
Segmentation of contrast-enhanced computed tomography (CECT) images enables quantitative evaluation of morphology of articular cartilage as well as the significance of the lesions. Unfortunately, automatic segmentation methods for CECT images are currently lacking. Here, we introduce a semiautomated technique to segment articular cartilage from in vivo CECT images of human knee. The segmented cartilage geometries of nine knee joints, imaged using a clinical CT-scanner with an intra-articular contrast agent, were compared with manual segmentations from CT and magnetic resonance (MR) images. The Dice similarity coefficients (DSCs) between semiautomatic and manual CT segmentations were 0.79–0.83 and sensitivity and specificity values were also high (0.76–0.86). When comparing semiautomatic and manual CT segmentations, mean cartilage thicknesses agreed well (intraclass correlation coefficient = 0.85–0.93); the difference in thickness (mean ± SD) was 0.27 ± 0.03 mm. Differences in DSC, when MR segmentations were compared with manual and semiautomated CT segmentations, were statistically insignificant. Similarly, differences in volume were not statistically significant between manual and semiautomatic CT segmentations. Semiautomation decreased the segmentation time from 450 ± 190 to 42 ± 10 min per joint. The results reveal that the proposed technique is fast and reliable for segmentation of cartilage. Importantly, this is the first study presenting semiautomated segmentation of cartilage from CECT images of human knee joint with minimal user interaction.
Annals of biomedical engineering
|Pages:||1756 - 1767|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
The authors acknowledge the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (Projects 5041746, 5041757, and 5203101). Study is also supported by Doctoral Program in Science, Technology and Computing (SCITECO, University of Eastern Finland), Finnish Cultural Foundation, and Academy of Finland (Projects 269315 and 307932).
© Biomedical Engineering Society 2018. This is a post-peer-review, pre-copyedit version of an article published in Annals of Biomedical Engineering. The final authenticated version is available online at: https://doi.org/10.1007/s10439-018-2081-z.