Knowledge of the premorbid glenoid shape and the morphological changes the bone undergoes in patients with glenohumeral arthritis can improve surgical outcomes in total and reverse shoulder arthroplasty. Several studies have previously used scapular statistical shape models (SSMs) to predict premorbid glenoid shape and evaluate glenoid erosion properties. However, current literature suggests no studies have used scapular SSMs to examine the changes in glenoid surface area in patients with glenohumeral arthritis. Therefore, the purpose of this study was to compare the glenoid articular surface area between pathologic glenoid cavities from patients with glenohumeral arthritis and their predicted premorbid shape using a scapular SSM. Furthermore, this study compared pathologic glenoid surface area with that from virtually eroded glenoid models created without influence from internal bone remodelling activity and osteophyte formation. It was hypothesized that the pathologic glenoid cavities would exhibit the greatest glenoid surface area despite the eroded nature of the glenoid and the medialization, which in a vault shape, should logically result in less surface area. Computer tomography (CT) scans from 20 patients exhibiting type A2 glenoid erosion according to the Walch classification [Walch et al., 1999] were obtained. A scapular SSM was used to predict the premorbid glenoid shape for each scapula. The scapula and humerus from each patient were automatically segmented and exported as 3D object files along with the scapular SSM from a pre-operative planning software. Each scapula and a copy of its corresponding SSM were aligned using the coracoid, lateral edge of the acromion, inferior glenoid tubercule, scapular notch, and the trigonum spinae. Points were then digitized on both the pathologic humeral and glenoid surfaces and were used in an iterative closest point (ICP) algorithm in MATLAB (MathWorks, Natick, MA, USA) to align the humerus with the glenoid surface. A Boolean subtraction was then performed between the scapular SSM and the humerus to create a virtual erosion in the scapular SSM that matched the erosion orientation of the pathologic glenoid. This led to the development of three distinct glenoid models for each patient: premorbid, pathologic, and virtually eroded (Fig. 1). The glenoid surface area from each model was then determined using 3-Matic (Materialise, Leuven, Belgium). Figure 1. (A) Premorbid glenoid model, (B) pathologic glenoid model, and (C) virtually eroded glenoid model. The average glenoid surface area for the pathologic scapular models was 70% greater compared to the premorbid glenoid models (P < 0 .001). Furthermore, the surface area of the virtual glenoid erosions was 6.4% lower on average compared to the premorbid glenoid surface area (P=0.361). The larger surface area values observed in the pathologic glenoid cavities suggests that sufficient bone remodelling exists at the periphery of the glenoid bone in patients exhibiting A2 type glenohumeral arthritis. This is further supported by the large difference in glenoid surface area between the pathologic and virtually eroded glenoid cavities as the virtually eroded models only considered humeral anatomy when creating the erosion. For any figures or tables, please contact the authors directly.
Reverse Total Shoulder Arthroplasty (rTSA) is an efficient treatment, to relieve from pain and to increase function. However, scapular notching remains a serious issue and post-operative range of motion (ROM) presents many variations. No study compared implant positioning, different implant combinations, different implant sizes on different types of patient representative to undergo for rTSA, on glenohumeral ROM in every degree of freedom. From a CT-scan database classified by a senior surgeon, CT-exams were analysed by a custom software Glenosys® (Imascap®, Brest, France). Different glenoid implants types and positioning were combined to different humerus implant types. Range of motion was automatically computed. Patients with an impingement in initialisation position were excluded from the statistical analysis. To validate those measures, a validation bench was printed in 3D to analyse different configurations.Introduction
Material and Methods
The glenoid version assessment is crucial step for any Total Shoulder Arthroplasty (TSA) procedure. New methods to compute 3D version angle of the glenoid have been proposed. These methods proposed different definitions of the glenoid plane and only used 3 points to define each plane on the 3D model of the scapula. In practice, patients often come to consultation with their CT-scans. In order to reduce the x-ray dose, the scapulae are often truncated on the inferior part. In these cases, the traditional scapula plane cannot be calculated. We hypothesised that a new plane definition, of the scapula and the glenoid, that takes into account all the 3D points, would have the least variation and provide more reliable measures whatever the scapula is truncated or not. The purpose of the study is to introduce new fully automatic method to compute 3D glenoid version for TSA preoperating planning and test its results on artificially truncated scapulae. Volumetric preoperative CT datasets have been used to derive a surface model shape of the shoulder. The glenoid surface is detected and a 3D version and inclination angle of the glenoid surface are computed. We propose a new reference plane of the scapula without picking points on the 3D model. The method is based on the mathematical skeleton of the scapula and the least squares plane fitting. Specific software has been developed to apply the plane fitting in addition the automatic segmentation process. An orthopedic surgeon defined the traditional scapular plane based on 3 points and applied the measures on 12 patients. The manual process has been repeated 3 times and the intra-class correlation coefficient (INTRODUCTION
MATERIAL AND METHODS
Automated MRI bone segmentation is one of the most challenging problems in medical imaging. To increase the segmentation robustness, a prior model of the structure could guide the segmentation. Statistical Shape Models (SSMs) are efficient examples for such application. We present an automated SSM construction approach of the The basic idea is to relate only corresponding parts of the shape under investigation. A sample from the samples set is chosen as a common reference (atlas), and the other samples are landmarked and registered to it so that the corresponding points can be identified. The registration has three levels: alignment, rigid and elastic transformations. To align two Afterwards, the samples are locally deformed toward the atlas using directly their landmarks (traditional approach). Unfortunately, landmarks-correspondences could be mismatched at some anatomically complex, “critical,” zones of the scapula. To overcome such a problem, we suggest to 3D-segment these “critical” zones using a 3D Watershed-based method. Watershed is based on a physical concept of immersion, where it is achieved in a similar way to water filling geographic basins. We believe that this is a natural way to segment the surface of the scapula since it has two large “basins”: the Once we have the zones, surface-to-surface correspondence is defined and the landmarks' point-to-point correspondences are obtained within each zone pair separately. The elastic registration is then applied on the whole surface via a multi-resolution B-Spline algorithm. The atlas is built through an iterative procedure to eliminate the bias to the initial choice and the correspondences are identified by a reverse registration. Finally, the statistical model can be constructed by performing Principle Component Analysis (PCA).INTRODUCTION
METHODS
For any image guided surgery, independently of the technique which is used (navigation, templates, robotics), it is necessary to get a 3D bone surface model from CT or MR images. Such model is used for planning, registration and visualization. We report that graphical representation of patient bony structure and the surgical tools, inter-connectively with the tracking device and patient-to-image registration, are crucial components in such system. For Total Shoulder Arthroplasty (TSA), there are many challenges. The most of cases that we are working with are pathological cases such as rheumatoid arthritis, osteoarthritis disease. The CT images of these cases often show a fusion area between the glenoid cavity and the humeral head. They also show severe deformations of the humeral head surface that result in a loss of contours. These fusion area and image quality problems are also amplified by well-known CT-scan artefacts like beam-hardening or partial volume effects. The state of the art shows that several segmentation techniques, applied to CT-Scans of the shoulder, have already been disclosed. Unfortunately, their performances, when used on pathological data, are quite poor. In severe cases, bone-on-bone arthritis may lead to erosion-wearing away of the bone. Shoulder replacement surgery, also called shoulder arthroplasty, is a successful, pain-relieving option for many people. During the procedure, the humeral head and the glenoid bone are replaced with metal and plastic components to alleviate pain and improve function. This surgical procedure is very difficult and limited to expert centres. The two main problems are the minimal surgical incision and limited access to the operated structures. The success of such procedure is related to optimal prosthesis positioning. For TSA, separating the humeral head in the 3D scanner images would allow enhancing the vision field for the surgeon on the glenoid surface. So far, none of the existing systems or software packages makes it possible to obtain such 3D surface model automatically from CT images and this is probably one of the reasons for very limited success of Computer Assisted Orthopaedic Surgery (CAOS) applications for shoulder surgery. This kind of application often has been limited due to CT-image segmentation for severe pathologic cases and patient to image registration. The aim of this paper is to present a new image guided planning software based on CT scan of the patient and using bony structure recognition, morphological and anatomical analysis for the operated region. Volumetric preoperative CT datasets have been used to derive a surface model shape of the shoulder. The proposed planning software could be used with a conventional localisation system, which locates in 3D and in real time position and orientation for surgical tools using passive markers associated to rigid bodies that will be fixed on the patient bone and on the surgical instruments. 20 series of patients aged from 42 years to 91 years (mean age of 71 years) were analysed. The first step of this planning software is fully automatic segmentation method based on 3D shape recognition algorithms applied to each object detected in the volume. The second step is a specific processing that only treats the region between the humerus and the glenoid surface in order to separate possible contact areas. The third step is a full morphological analysis of anatomical structure of the bone. The glenoid surface and the glenoid vault are detected and a 3D version and inclination angle of the glenoid surface are computed. These parameters are very important to define an optimal path for drilling and reaming glenoid surface. The surgeon can easily modify the position of the implant in 3D aided by 3D and 2D view of the patient anatomy. The glenoid version/inclination angle and the glenoid vault are computed for each postion in real time to help the surgeon to evaluate the implant position and orientation. In summary, preoperative planning, 3D CT modelling and intraoperative tracking produced improved accuracy of glenoid implantation. The current paper has presented new planning software in the world of image guided surgery focused on shoulder arthroplasty. Within our approach, we propose, to use pattern recognition instead of manual picking of landmarks to avoid user intervention, in addition to potentially reducing the procedure time. A very important role is played by 3D data sets to visualise specific anatomical structures of the patient. The automatic segmentation of arthritic joints with bone recognition is intended to form a solid basis for the registration. The results of this methodology were tested on arthritic patients to prove that it is not just easy and fast to perform but also very accurate so it realises all conditions for the clinical use in OR.
