Melissa R Requist1,2, Yantarat Sripanich1,3, Andrew C Peterson1, Tim Rolvien4, Alexej Barg5,6, Amy L Lenz7. 1. Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA. 2. Department of Biomedical Engineering, University of Arizona, 1127 E James E Rogers Way, Tucson, AZ, 85721, USA. 3. Department of Orthopaedics, Phramongkutklao Hospital and College of Medicine, 315 Rajavithi Road, Tung Phayathai, Ratchathewi, Bangkok, 10400, Thailand. 4. Department of Trauma and Orthopaedic Surgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany. 5. Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA. al.barg@uke.de. 6. Department of Trauma and Orthopaedic Surgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany. al.barg@uke.de. 7. Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA. amy.lenz@utah.edu.
Abstract
PURPOSE: In the field of skeletal research, accurate and reliable segmentation methods are necessary for quantitative micro-CT analysis to assess bone quality. We propose a method of semi-automatic image segmentation of the midfoot, using the cuneiform bones as a model, based on thresholds set by phantom calibration that allows reproducible results in low cortical thickness bones. METHODS: Manual and semi-automatic segmentation methods were compared in micro-CT scans of the medial and intermediate cuneiforms of 24 cadaveric specimens. The manual method used intensity thresholds, hole filling, and manual cleanup. The semi-automatic method utilized calibrated bone and soft tissue thresholds Boolean subtraction to cleanly identify edges before hole filling. Intra- and inter-rater reliability was tested for the semi-automatic method in all specimens. Mask volume and average bone mineral density (BMD) were measured for all masks, and the three-dimensional models were compared to the initial semi-automatic segmentation using an unsigned distance part comparison analysis. Segmentation methods were compared with paired t-tests with significance level 0.05, and reliability was analyzed by calculating intra-class correlation coefficients. RESULTS: There were statistically significant differences in mask volume and BMD between the manual and semi-automatic segmentation methods in both bones. The intra- and inter-reliability was excellent for mask volume and bone density in both bones. Part comparisons showed a higher maximum distance between surfaces for the manual segmentation than the repeat semi-automatic segmentations. CONCLUSION: We developed a semi-automatic micro-CT segmentation method based on calibrated thresholds. This method was designed specifically for use in bones with high rates of curvature and low cortical bone density, such as the cuneiforms, where traditional threshold-based segmentation is more challenging. Our method shows improvement over manual segmentation and was highly reliable, making it appropriate for use in quantitative micro-CT analysis.
PURPOSE: In the field of skeletal research, accurate and reliable segmentation methods are necessary for quantitative micro-CT analysis to assess bone quality. We propose a method of semi-automatic image segmentation of the midfoot, using the cuneiform bones as a model, based on thresholds set by phantom calibration that allows reproducible results in low cortical thickness bones. METHODS: Manual and semi-automatic segmentation methods were compared in micro-CT scans of the medial and intermediate cuneiforms of 24 cadaveric specimens. The manual method used intensity thresholds, hole filling, and manual cleanup. The semi-automatic method utilized calibrated bone and soft tissue thresholds Boolean subtraction to cleanly identify edges before hole filling. Intra- and inter-rater reliability was tested for the semi-automatic method in all specimens. Mask volume and average bone mineral density (BMD) were measured for all masks, and the three-dimensional models were compared to the initial semi-automatic segmentation using an unsigned distance part comparison analysis. Segmentation methods were compared with paired t-tests with significance level 0.05, and reliability was analyzed by calculating intra-class correlation coefficients. RESULTS: There were statistically significant differences in mask volume and BMD between the manual and semi-automatic segmentation methods in both bones. The intra- and inter-reliability was excellent for mask volume and bone density in both bones. Part comparisons showed a higher maximum distance between surfaces for the manual segmentation than the repeat semi-automatic segmentations. CONCLUSION: We developed a semi-automatic micro-CT segmentation method based on calibrated thresholds. This method was designed specifically for use in bones with high rates of curvature and low cortical bone density, such as the cuneiforms, where traditional threshold-based segmentation is more challenging. Our method shows improvement over manual segmentation and was highly reliable, making it appropriate for use in quantitative micro-CT analysis.
Authors: Erlend Peter Skaug Sande; Anne Catrine Traegde Martinsen; Eli Olaug Hole; Hilde M Olerud Journal: Phys Med Biol Date: 2010-08-16 Impact factor: 3.609
Authors: Hugo Giambini; Dan Dragomir-Daescu; Paul M Huddleston; Jon J Camp; Kai-Nan An; Ahmad Nassr Journal: J Biomech Eng Date: 2015-11 Impact factor: 2.097
Authors: Klaus Engelke; Thomas Lang; Sundeep Khosla; Ling Qin; Philippe Zysset; William D Leslie; John A Shepherd; John T Schousboe Journal: J Clin Densitom Date: 2015 Jul-Sep Impact factor: 2.617