surgeryglenoid-paper.bib

@misc{blueprint,
  title = {Blueprint {3D} Planning Software},
  howpublished = {\url{https://www.wright.com/blueprint-3d-planning-psi-system}},
  note = {Accessed: 2021-07-27}
}
@misc{exactech,
  title = {Exactech Equinox},
  howpublished = {\url{https://www.exac.com/extremities/equinoxe-platform-system/}},
  note = {Accessed: 2021-07-27}
}

@misc{djosurgical, 
	title = {{DJO} Surgical},
	howpublished = {\url{https://www.djoglobal.com/our-brands/djo-surgical}},
	note = {Accessed: 2021-08-04}
}


@article {PMID:29298261,
	Title = {Automated Three-Dimensional Measurement of Glenoid Version and Inclination in Arthritic Shoulders},
	Author = {Boileau, Pascal and Cheval, Damien and Gauci, Marc-Olivier and Holzer, Nicolas and Chaoui, Jean and Walch, Gilles},
	url = {https://doi.org/10.2106/jbjs.16.01122},
	Number = {1},
	Volume = {100},
	Month = {January},
	Year = {2018},
	Journal = {The Journal of bone and joint surgery. American volume},
	ISSN = {0021-9355},
	Pages = {57—65},
	Abstract = {BACKGROUND:Preoperative computed tomography (CT) measurements of glenoid version and inclination are recommended for planning glenoid implantation in shoulder arthroplasty. However, current manual or semi-automated 2-dimensional (2D) and 3-dimensional (3D) methods are user-dependent and time-consuming. We assessed whether the use of a 3D automated method is accurate and reliable to measure glenoid version and inclination in osteoarthritic shoulders. METHODS:CT scans of osteoarthritic shoulders of 60 patients scheduled for shoulder arthroplasty were obtained. Automated, surgeon-operated, image analysis software (Glenosys; Imascap) was developed to measure glenoid version and inclination. The anatomic scapular reference planes were defined as the mean of the peripheral points of the scapular body as well as the plane perpendicular to it, passing along the supraspinatus fossa line. Measurements were compared with those obtained using previously described manual or semi-automated methods, including the Friedman version angle on 2D CTs, Friedman method on 3D multiplanar reconstructions (corrected Friedman method), Ganapathi-Iannotti and Lewis-Armstrong methods on 3D volumetric reconstructions (for glenoid version), and Maurer method (for glenoid inclination).The mean differences (and standard deviation) and the concordance correlation coefficients (CCCs) were calculated. Two orthopaedic surgeons independently examined the images for the interobserver analysis, with one of them measuring them twice more for the intraobserver analysis; interobserver and intraobserver reliability was calculated using the intraclass correlation coefficients (ICCs). RESULTS:The mean difference in the Glenosys glenoid version measurement was 2.0° ± 4.5° (CCC = 0.93) compared with the Friedman method, 2.5° ± 3.2° (CCC = 0.95) compared with the corrected Friedman method, 1.5° ± 4.5° (CCC = 0.94) compared with the Ganapathi-Iannotti method, and 1.8° ± 3.8° (CCC = 0.95) compared with the Lewis-Armstrong method. There was a mean difference of 0.2° ± 4.7° (CCC = 0.78) between the inclination measurements made with the Glenosys and Maurer methods. The difference between the overall average 2D and 3D measurements was not significant (p = 0.45). CONCLUSIONS:Use of fully automated software for 3D measurement of glenoid version and inclination in arthritic shoulders is reliable and accurate, showing excellent correlation with previously described manual or semi-automated methods. CLINICAL RELEVANCE:The use of automated surgeon-operated image analysis software to evaluate 3D glenoid anatomy eliminates interobserver and intraobserver discrepancies, improves the accuracy of preoperative planning for shoulder replacement, and offers a potential gain of time for the surgeon.},
	URL = {https://doi.org/10.2106/JBJS.16.01122},
}

@article {PMID:32010231,
	Title = {Automated three-dimensional measurements of version, inclination, and subluxation},
	Author = {Shukla, Dave R and McLaughlin, Richard J and Lee, Julia and Nguyen, Ngoc Tram V and Sanchez-Sotelo, Joaquin},
	url = {https://doi.org/10.1177/1758573218825480},
	Number = {1},
	Volume = {12},
	Month = {February},
	Year = {2020},
	Journal = {Shoulder \& elbow},
	ISSN = {1758-5732},
	Pages = {31—37},
	Abstract = {<h4>Background</h4>Preoperative planning software has been developed to measure glenoid version, glenoid inclination, and humeral head subluxation on computed tomography (CT) for shoulder arthroplasty. However, most studies analyzing the effect of glenoid positioning on outcome were done prior to the introduction of planning software. Thus, measurements obtained from the software can only be extrapolated to predict failure provided they are similar to classic measurements. The purpose of this study was to compare measurements obtained using classic manual measuring techniques and measurements generated from automated image analysis software.<h4>Methods</h4>Ninety-five two-dimensional computed tomography scans of shoulders with primary glenohumeral osteoarthritis were measured for version according to Friedman method, inclination according to Maurer method, and subluxation according to Walch method. DICOM files were loaded into an image analysis software (Blueprint, Wright Medical) and the output was compared with values obtained manually using a paired sample <i>t</i>-test.<h4>Results</h4>Average manual measurements included 13.8° version, 13.2° inclination, and 56.2% subluxation. Average image analysis software values included 17.4° version (3.5° difference, <i>p</i> < 0.0001), 9.2° inclination (3.9° difference, <i>p</i> < 0.001), and 74.2% for subluxation (18% difference, <i>p</i> < 0.0001).<h4>Conclusions</h4>Glenoid version and inclination values from the software and manual measurement on two-dimensional computed tomography were relatively similar, within approximately 4°. However, subluxation measurements differed by approximately 20%.},
}

@article {PMID:33330245,
	Title = {Comparative study of glenoid version and inclination using two-dimensional images from computed tomography and three-dimensional reconstructed bone models},
	Author = {Choi, Chang-Hyuk and Kim, Hee-Chan and Kang, Daewon and Kim, Jun-Young},
	url = {https://doi.org/10.5397/cise.2020.00220},
	Number = {3},
	Volume = {23},
	Month = {September},
	Year = {2020},
	Journal = {Clinics in shoulder and elbow},
	ISSN = {2383-8337},
	Pages = {119—124},
	Abstract = {<h4>Background</h4>This study was performed to compare glenoid version and inclination measured using two-dimensional (2D) images from computed tomography (CT) scans or three-dimensional (3D) reconstructed bone models.<h4>Methods</h4>Thirty patients who had undergone conventional CT scans were included. Two orthopedic surgeons measured glenoid version and inclination three times on 2D images from CT scans (2D measurement), and two other orthopedic surgeons performed the same measurements using 3D reconstructed bone models (3D measurement). The 3D-reconstructed bone models were acquired and measured with Mimics and 3-Matics (Materialise).<h4>Results</h4>Mean glenoid version and inclination in 2D measurements were -1.705º and 9.08º, respectively, while those in 3D measurements were 2.635º and 7.23º. The intra-observer reliability in 2D measurements was 0.605 and 0.698, respectively, while that in 3D measurements was 0.883 and 0.892. The inter-observer reliability in 2D measurements was 0.456 and 0.374, respectively, while that in 3D measurements was 0.853 and 0.845.<h4>Conclusions</h4>The difference between 2D and 3D measurements is not due to differences in image data but to the use of different tools. However, more consistent results were obtained in 3D measurement. Therefore, 3D measurement can be a good alternative for measuring glenoid version and inclination.},
}
@article {PMID:33554174,
	Title = {Intraoperative navigation and preoperative templating software are associated with increased glenoid baseplate screw length and use of augmented baseplates in reverse total shoulder arthroplasty},
	Author = {Sprowls, Gregory R and Wilson, Charlie D and Stewart, Wells and Hammonds, Kendall A P and Baruch, Nathan H and Ward, Russell A and Robin, Brett N},
	url = {https://doi.org/10.1016/j.jseint.2020.09.003},
	Number = {1},
	Volume = {5},
	Month = {January},
	Year = {2021},
	Journal = {JSES international},
	ISSN = {2666-6383},
	Pages = {102—108},
	Abstract = {<h4>Background</h4>Preoperative templating software and intraoperative navigation have the potential to impact baseplate augmentation utilization and increase screw length for baseplate fixation in reverse total shoulder arthroplasty (rTSA). We aimed to assess their impact on the (1) baseplate screw length, (2) number of screws used, and (3) frequency of augmented baseplate use in navigated rTSA.<h4>Methods</h4>We compared 51 patients who underwent navigated rTSA with 63 controls who underwent conventional rTSA at a single institution. Primary outcomes included the screw length, composite screw length, number of screws used, percentage of patients in whom 2 screws in total were used, and use of augmented baseplates.<h4>Results</h4>Navigation resulted in the use of significantly longer individual screws (36.7 mm vs. 30 mm, <i>P</i> < .0001), greater composite screw length (84 mm vs. 76 mm, <i>P</i> = .048), and fewer screws (2.5 ± 0.7 vs. 2.8 ± 1, <i>P</i> = .047), as well as an increased frequency of using 2 screws in total (35 of 51 patients [68.6%] vs. 32 of 63 controls [50.8%], <i>P</i> = .047). Preoperative templating resulted in more frequent augmented baseplate utilization (76.5% vs. 19.1%, <i>P</i> < .0001).<h4>Conclusion</h4>The difference in the screw length, number of screws used, and augmented baseplate use demonstrates the evolving role that computer navigation and preoperative templating play in surgical planning and the intraoperative technique for rTSA.},
}
@article {PMID:1522089,
	Title = {The use of computerized tomography in the measurement of glenoid version},
	Author = {Friedman, RJ and Hawthorne, KB and Genez, BM},
	Number = {7},
	Volume = {74},
	Month = {August},
	Year = {1992},
	Journal = {The Journal of bone and joint surgery. American volume},
	ISSN = {0021-9355},
	Pages = {1032—1037},
	Abstract = {Computerized tomography was done preoperatively on twenty shoulders (thirteen patients) in which there were severe arthritic changes, to measure glenoid version. Ten of the twenty shoulders had osteoarthrosis; eight, rheumatoid arthritis; and two, gouty arthritis. To help determine normal values, computerized tomographic scans of the chest of sixty-three patients who did not have roentgenographic evidence of disease of the shoulder were studied retrospectively for comparison as a control group. In the group of patients who had severe arthritis, the mean glenoid orientation was 11 degrees of retroversion (range, 2 degrees of anteversion to 32 degrees of retroversion). The computerized tomographic scans showed uneven wear of the glenoid surface, osteophytes, large cysts, and posterior displacement of the humeral head. In the control group, the mean orientation of the glenoid was 2 degrees of anteversion (range, 14 degrees of anteversion to 12 degrees of retroversion). The difference between the groups was significant (p less than 0.0001). Glenoid retroversion was increased in the patients who had severe arthritis, and the computerized tomographic scans accurately revealed the extent and pattern of erosion of the bone.},
	URL = {http://europepmc.org/abstract/MED/1522089}
}

@article {PMID:32436132,
	Title = {SciKit-Surgery: compact libraries for surgical navigation},
	Author = {Thompson, Stephen and Dowrick, Thomas and Ahmad, Mian and Xiao, Goufang and Koo, Bongjin and Bonmati, Ester and Kahl, Kim and Clarkson, Matthew J},
	url = {https://doi.org/10.1007/s11548-020-02180-5},
	Number = {7},
	Volume = {15},
	Month = {July},
	Year = {2020},
	Journal = {International journal of computer assisted radiology and surgery},
	ISSN = {1861-6410},
	Pages = {1075—1084},
	Abstract = {<h4>Purpose</h4>This paper introduces the SciKit-Surgery libraries, designed to enable rapid development of clinical applications for image-guided interventions. SciKit-Surgery implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation. The aim is to support translation from single surgeon trials to multicentre trials in under 2 years.<h4>Methods</h4>At the time of publication, there were 13 SciKit-Surgery libraries provide functionality for visualisation and augmented reality in surgery, together with hardware interfaces for video, tracking, and ultrasound sources. The libraries are stand-alone, open source, and provide Python interfaces. This design approach enables fast development of robust applications and subsequent translation. The paper compares the libraries with existing platforms and uses two example applications to show how SciKit-Surgery libraries can be used in practice.<h4>Results</h4>Using the number of lines of code and the occurrence of cross-dependencies as proxy measurements of code complexity, two example applications using SciKit-Surgery libraries are analysed. The SciKit-Surgery libraries demonstrate ability to support rapid development of testable clinical applications. By maintaining stricter orthogonality between libraries, the number, and complexity of dependencies can be reduced. The SciKit-Surgery libraries also demonstrate the potential to support wider dissemination of novel research.<h4>Conclusion</h4>The SciKit-Surgery libraries utilise the modularity of the Python language and the standard data types of the NumPy package to provide an easy-to-use, well-tested, and extensible set of tools for the development of applications for image-guided interventions. The example application built on SciKit-Surgery has a simpler dependency structure than the same application built using a monolithic platform, making ongoing clinical translation more feasible.},
}

@article{Fu2020,
  url = {https://doi.org/10.21105/joss.02705},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {55},
  pages = {2705},
  author = {Yunguan Fu and Nina Montaña Brown and Shaheer U. Saeed and Adrià Casamitjana and Zachary M. c. Baum and Rémi Delaunay and Qianye Yang and Alexander Grimwood and Zhe Min and Stefano B. Blumberg and Juan Eugenio Iglesias and Dean C. Barratt and Ester Bonmati and Daniel C. Alexander and Matthew J. Clarkson and Tom Vercauteren and Yipeng Hu},
  title = {DeepReg: a deep learning toolkit for medical image registration},
  journal = {Journal of Open Source Software}
}

@article {PMID:20933439,
	Title = {Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models},
	Author = {Ganapathi, Asvin and McCarron, Jesse A and Chen, Xi and Iannotti, Joseph P},
	url = {https://doi.org/10.1016/j.jse.2010.05.024},
	Number = {2},
	Volume = {20},
	Month = {March},
	Year = {2011},
	Journal = {Journal of shoulder and elbow surgery},
	ISSN = {1058-2746},
	Pages = {234—244},
	Abstract = {<h4>Background</h4>Correction of pathologic glenoid retroversion improves gleonhumeral mechanics and reduces glenoid component wear after total shoulder arthroplasty. Determining the amount of correction necessary can be difficult because of the wide range of normal glenoid version. We hypothesize that normal glenoid version can be predicted in a pathologic shoulder based on conserved relationships between the anterior glenoid wall, Resch angle, and the internal structures of the glenoid vault.<h4>Materials and methods</h4>Three-dimensional (3-D) computer tomography (CT) scan-based measurements of the anterior glenoid wall angle (AGWA), Resch angle (RA), and glenoid version were made in 58 scapulae from the Haeman-Todd Osteological Collection (Museum of Natural History in Cleveland, OH) and 19 paired scapulae from patients with unilateral osteoarthritis. Linear regression equations derived from the AGWA and RA and from a computer-generated vault model were used to predict native (nonpathologic) glenoid version as defined by the 19 nonpathologic scapula.<h4>Results</h4>Linear regression equations based on the measured AGWA or RA, as well as the glenoid vault model in the 19 pathologic scapulae, were able to accurately predict native glenoid version in the contralateral nonpathologic shoulder.<h4>Discussion</h4>This study demonstrates the ability to take 3-D CT scan-based measurements in a scapula with pathologic glenoid retroversion and predict the native (nonpathologic) glenoid version in the contralateral shoulder by using linear regression equations or a computer generated vault model. Such tools might assist in preoperative planning and intraoperative decision making to allow correction of pathologic glenoid retroversion.},
	URL = {https://doi.org/10.1016/j.jse.2010.05.024},
}


@article {PMID:24618285,
	Title = {Computed tomography measurement of glenoid vault version as an alternative measuring method for glenoid version},
	Author = {Matsumura, Noboru and Ogawa, Kiyohisa and Ikegami, Hiroyasu and Collin, Philippe and Walch, Gilles and Toyama, Yoshiaki},
	url = {https://doi.org/10.1186/1749-799x-9-17},
	Number = {1},
	Volume = {9},
	Month = {March},
	Year = {2014},
	Journal = {Journal of orthopaedic surgery and research},
	ISSN = {1749-799X},
	Pages = {17},
	Abstract = {<h4>Background</h4>The conventional measuring method for glenoid version is greatly influenced by the scapular body shape that varies widely between patients. We postulated that the glenoid vault version could be more useful than the conventional glenoid version in clinical cases.<h4>Objectives</h4>The purposes of this study were to compare the values of glenoid version measured with the conventional method to those with the vault method and to investigate the feasibility of the glenoid vault version.<h4>Methods</h4>Computed tomography scans of 150 normal shoulders and 150 arthritic shoulders were analyzed. Three-dimensionally corrected slices were reconstructed from the Digital Imaging and Communications in Medicine (DICOM) data, and glenoid version was measured with both the conventional and vault methods. After determining intra- and interrater reliabilities, differences in glenoid version values between the conventional and vault methods were assessed. In the normal shoulder group, side-to-side differences of glenoid version values were also evaluated in both methods.<h4>Results</h4>Both measuring methods demonstrated high intra- and interrater reliabilities. The normal glenoid had 1.1° ± 3.2° retroversion with the conventional method and 8.9° ± 2.7° retroversion with the vault method. The average glenoid retroversion of arthritic shoulders was 10.8° ± 9.3° measured with the conventional method and 18.2° ± 9.1° with the vault method. The vault method showed significantly larger glenoid retroversion than the conventional method in both normal and arthritic shoulder groups. Both conventional glenoid retroversion and glenoid vault retroversion were significantly larger on dominant sides than on nondominant sides in the normal shoulders.<h4>Conclusions</h4>The glenoid vault version could be used as an alternative measuring method for glenoid version with high reliability. In clinical use, the glenoid vault version appears to be more useful than the conventional glenoid version to assess the severity of arthritis and difficulty of glenoid replacement. The glenoid vault is not symmetric, but usually retroverted in both normal and arthritic shoulders.},
}

@InProceedings{2004PieperSlicer3D,
  Title                    = {{3D Slicer}},
  Author                   = {Pieper, Steve and Halle, Michael and Kikinis, Ron},
  Booktitle                = {Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on},
  Year                     = {2004},
  Organization             = {IEEE},
  Pages                    = {632--635},

  Owner                    = {mattclarkson},
  Timestamp                = {2013.04.29},
  Url                      = {http://www.slicer.org}
}

@Inbook{Kikinis2014,
author="Kikinis, Ron
and Pieper, Steve D.
and Vosburgh, Kirby G.",
editor="Jolesz, Ferenc A.",
title="3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support",
bookTitle="Intraoperative Imaging and Image-Guided Therapy",
year="2014",
publisher="Springer New York",
address="New York, NY",
pages="277--289",
abstract="3D Slicer is an open-source platform for the analysis and display of information derived from medical imaging and similar data sets. Such advanced software environments are in daily use by researchers and clinicians and in many nonmedical applications. 3D Slicer is unique through serving clinical users, multidisciplinary clinical research terms, and software architects within a single technology structure and user community. Functions such as interactive visualization, image registration, and model-based analysis are now being complemented by more advanced capabilities, most notably in neurological imaging and intervention. These functions, originally limited to offline use by technical factors, are integral to large scale, rapidly developing research studies, and they are being increasingly integrated into the management and delivery of care. This activity has been led by a community of basic, applied, and clinical scientists and engineers, from both academic and commercial perspectives. 3D Slicer, a free open-source software package, is based in this community; 3D Slicer provides a set of interactive tools and a stable platform that can quickly incorporate new analysis techniques and evolve to serve more sophisticated real-time applications while remaining compatible with the latest hardware and software generations of host computer systems.",
isbn="978-1-4614-7657-3",
url="https://doi.org/10.1007/978-1-4614-7657-3_19"
}

@article {PMID:33937966,
	Title = {Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware},
	Author = {Shapey, Jonathan and Dowrick, Thomas and Delaunay, Rémi and Mackle, Eleanor C and Thompson, Stephen and Janatka, Mirek and Guichard, Roland and Georgoulas, Anastasis and Pérez-Suárez, David and Bradford, Robert and Saeed, Shakeel R and Ourselin, Sébastien and Clarkson, Matthew J and Vercauteren, Tom},
	url = {https://doi.org/10.1007/s11548-021-02374-5},
	Number = {8},
	Volume = {16},
	Month = {August},
	Year = {2021},
	Journal = {International journal of computer assisted radiology and surgery},
	ISSN = {1861-6410},
	Pages = {1347—1356},
	Abstract = {<h4>Purpose</h4>Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery.<h4>Methods</h4>We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system's ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model.<h4>Results</h4>Experimental validation of the system's navigated ultrasound component demonstrated accuracy of [Formula: see text] and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of [Formula: see text] and performed with a clinically acceptable level of accuracy.<h4>Conclusion</h4>We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.},
}

@article{BUDGE2011577,
title = {Comparison of standard two-dimensional and three-dimensional corrected glenoid version measurements},
journal = {Journal of Shoulder and Elbow Surgery},
volume = {20},
number = {4},
pages = {577-583},
year = {2011},
issn = {1058-2746},
url = {https://doi.org/10.1016/j.jse.2010.11.003},
author = {Matthew D. Budge and Gregory S. Lewis and Eric Schaefer and Stephanie Coquia and Donald J. Flemming and April D. Armstrong},
keywords = {Glenoid version, 3D reconstruction, total shoulder arthroplasty, glenoid component, placement},
abstract = {Hypothesis
There is concern regarding the accuracy of 2-dimensional (2D) computed tomography (CT) for measuring glenoid version. Three-dimensional (3D) CT scan reconstructions can properly orient the glenoid to the plane of the scapula and have been reported to accurately measure glenoid version in cadaver models. We hypothesized that glenoid version measured by correcting 2D CT scans to the plane of the scapula by 3D reconstruction would be significantly different compared with standard 2D CT scan measurement of the glenoid in a clinical patient population.
Materials and methods
Thirty-four patients underwent dedicated axial 2D CT scan of the shoulder with 3D reconstruction. The 2D glenoid version was measured on unmodified midglenoid axial cuts, and the 3D glenoid version measurement was corrected to be perpendicular to the plane of the scapula and then measured in the axial plane. Three observers repeated each measurement on 2 different days.
Results
The difference between the overall average 2D and 3D measurements was not statistically significant (P = .45). In individual scapulae, 35% of 2D measurements were 5° to 10° different and 12% were greater than 10° different from their corresponding 3D-corrected CT measurement (P < .001 to P = .045). Reproducibility of both 2D and 3D-corrected measurements was good.
Discussion
Although 2D and 3D corrected methods showed a high degree of both intraobserver and interobserver reliability in this series, axial 2D images without correction were 5 to 15 degrees different than their 3D-corrected counterparts in 47% of all measurements. Correcting 2D glenoid version by 3D reconstruction to the transverse plane perpendicular to the scapular body allows for an accurate assessment of glenoid version in spite of positioning differences and results in increased accuracy while maintaining high reliability.
Conclusions
Owing to the variability in scapular position, the axial 2D CT scan measurement was significantly different from 3D-corrected measurement of glenoid version. Averaging the version measurements across patients did not reflect this finding.}
}

@article {PMID:22964089,
	Title = {Three-dimensional measurement method of arthritic glenoid cavity morphology: feasibility and reproducibility},
	Author = {Moineau, G and Levigne, C and Boileau, P and Young, A and Walch, G and {French Society for Shoulder and Elbow (SOFEC)}},
	Number = {6 Suppl},
	Volume = {98},
	Month = {October},
	Year = {2012},
	Journal = {Orthopaedics and traumatology, surgery and research : OTSR},
	ISSN = {1877-0568},
	Pages = {S139—45},
	URL = {https://doi.org/10.1016/j.otsr.2012.06.007},
}

@article {PMID:29778592,
	Title = {Comparative analysis of 2 glenoid version measurement methods in variable axial slices on 3-dimensionally reconstructed computed tomography scans},
	Author = {Cunningham, Gregory and Freebody, John and Smith, Margaret M and Taha, Mohy E and Young, Allan A and Cass, Benjamin and Giuffre, Bruno},
	Number = {10},
	Volume = {27},
	Month = {October},
	Year = {2018},
	Journal = {Journal of shoulder and elbow surgery},
	ISSN = {1058-2746},
	Pages = {1809—1815},
	URL = {https://doi.org/10.1016/j.jse.2018.03.016},
}

@article{bang2011,
author = {Wong, Bang},
year = {2011},
month = {06},
pages = {441},
title = {Points of view: Color blindness},
volume = {8},
journal = {Nature methods},
url = {https://doi.org/10.1038/nmeth.1618}
}

@article{thompson2020snappysonic,
  title={SnappySonic: An Ultrasound Acquisition Replay Simulator},
  author={Thompson, Stephen and Dowrick, Thomas and Xiao, Goufang and Ramalhinho, Jo{\~a}o and Robu, Maria and Ahmad, Mian and Taylor, Dan and Clarkson, Matthew J},
  journal={Journal of Open Research Software},
  volume={8},
  number={1},
  year={2020},
  publisher={Ubiquity Press},
  url={http://doi.org/10.5334/jors.289}
}

@article{dowrick2021cmakecatchtemplate,
  title={CMakeCatchTemplate: A C++ template project},
  author={Dowrick, Thomas and Ahmad, Mian and Thompson, Stephen and Hetherington, James and Cooper, Jonathan and Clarkson, Matt},
  journal={Journal of Open Research Software},
  volume={9},
  number={1},
  year={2021},
  publisher={Ubiquity Press},
  url={http://doi.org/10.5334/jors.319}
}

@inproceedings{thompson2021fiducial,
  title={Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research},
  author={Thompson, Stephen and Dowrick, Tom and Ahmad, Mian and Opie, Jeremy and Clarkson, Matthew J},
  booktitle={Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling},
  volume={11598},
  pages={115980U},
  year={2021},
  organization={International Society for Optics and Photonics},
  url={https://doi.org/10.1117/12.2580159}
}

@article{schneider2020comparison,
  title={Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study},
  author={Schneider, C and Thompson, S and Totz, J and Song, Y and Allam, M and Sodergren, MH and Desjardins, AE and Barratt, D and Ourselin, S and Gurusamy, K and others},
  journal={Surgical endoscopy},
  volume={34},
  number={10},
  pages={4702--4711},
  year={2020},
  publisher={Springer},
  url={https://doi.org/10.1007/s00464-020-07807-x}
}

@software{olafsdottir2022,
  author       = {Olafsdottir, Asta and
                  Thompson, Stephen},
  title        = {SciKit-SurgeryGlenoid},
  month        = jan,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v0.0.1},
  url          = {https://doi.org/10.5281/zenodo.5901818}
}

@software{doel_tom_2022_5879146,
  author       = {Doel, Tom and
                  Thompson, Stephen and
                  Dowrick, Thomas and
                  Ahmad, Mian and
                  Clarkson, Matthew},
  title        = {Python Template},
  month        = jan,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v1.1},
  url          = {https://doi.org/10.5281/zenodo.5879146}
}

@ARTICLE{2020NumPy-Array,
  author  = {Harris, Charles R. and Millman, K. Jarrod and
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            Fernández del Río, Jaime and Wiebe, Mark and
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            Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and
            Abbasi, Hameer and Gohlke, Christoph and
            Oliphant, Travis E.},
  title   = {Array programming with {NumPy}},
  journal = {Nature},
  year    = {2020},
  volume  = {585},
  pages   = {357–362},
  url     = {https://doi.org/10.1038/s41586-020-2649-2}
}

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@article{bryce2010two,
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@article{nyffeler2003measurement,
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@inproceedings{schroeder2004software,
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  pages={648--651},
  year={2004},
  organization={IEEE}
}