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SciKit-SurgeryGlenoid, an Open Source Toolkit for Glenoid Version Measurement.
Olafsdottir, Asta; Majed, Addie; Butt, David; Falworth, Mark; Clarkson, Matthew J; Thompson, Stephen.
  • Olafsdottir A; Wellcome/EPSRC Centre for Interventional and Surgical Science, University College London, United Kingdom.
  • Majed A; The Royal National Orthopaedic Hospital NHS Trust.
  • Butt D; The Royal National Orthopaedic Hospital NHS Trust.
  • Falworth M; The Royal National Orthopaedic Hospital NHS Trust.
  • Clarkson MJ; Wellcome/EPSRC Centre for Interventional and Surgical Science, University College London, United Kingdom.
  • Thompson S; Wellcome/EPSRC Centre for Interventional and Surgical Science, University College London, United Kingdom.
Proc SPIE Int Soc Opt Eng ; 12034: 120341S, 2022 Apr 04.
Article en En | MEDLINE | ID: mdl-37767103
ABSTRACT
Correct understanding of the geometry of the glenoid (the socket of the shoulder joint) is key to successful planning of shoulder replacement surgery. This surgery typically involves placing an implant in the shoulder joint to restore joint function. The most relevant geometry is the glenoid version, which is the angular orientation of the glenoid surface relative to the long axis of the scapula in the axial plane. However, measuring the glenoid version is not straightforward and there are multiple measurement methods in the literature and used in commercial planning software. In this paper we introduce SciKit-SurgeryGlenoid, an open source toolkit for the measurement of glenoid version. SciKit-SurgeryGlenoid contains implementations of the 4 most frequently used glenoid version measurement algorithms enabling easy and unbiased comparison of the different techniques. We present the results of using the software on 10 sets of pre-operative CT scans taken from patients who have subsequently undergone shoulder replacement surgery. We further compare these results with those obtained from a commercial implant planning software. SciKit-SurgeryGlenoid currently requires manual segmentation of the relevant anatomical features for each method. Future work will look at automating the segmentation process to build an automatic and repeatable pipeline from CT or radiograph to quantitative glenoid version measurement.