Bone shape estimation from partial observations, such as fluoroscopy or ultrasound, has been subject of significant interest over the past decade and can be regarded as the driving force behind several advances in statistical modelling of shape. While statistical models were initially used mostly as regularisers constraining shape matching algorithms, they are now increasingly employed due to their predictive ability, when only limited observations are available. With the current efforts toward minimal invasiveness, radiation exposure reduction, and optimization of the cost-effectiveness of procedures, two major challenges emerge on the field of statistical modelling. The first one is to develop methods that enable the use of as much information as possible that can be relevant for a specific shape prediction task, within the aforementioned limits. The second challenge concerns the accuracy of the resulting predictions, which needs to be quantified in order to evaluate the associated risks, and to optimise the data acquisition procedures. In terms of shape prediction, most studies so far have concentrated on individualizing statistical atlases based on imaging data. However, relevant information about skeletal morphology can also be obtained from simple anthropometric and morphometric measurements such as gender, age, body-mass index, and bone specific measurements. We develop a multivariate regression framework that enables to take into account such combinations of predictors simultaneously with sparse observations of the bone surface for improved prediction of the complete bone shape. In particular, we describe in a quantitative and localised fashion the individual contributions but also the complementarities of the heterogeneous sources of information with respect to bone morphology assessment. To do so, we compare the prediction errors obtained with different combinations of predictors, relying on cross-validation experiments. In addition to providing valuable and complementary predictive information, non-imaging measurements can be exploited to automatically initialise surface registration algorithms which increase their robustness for the determination of patient specific morphologies. A statistical model, by essence, is a mathematical model resulting from a learning phase using a set of training data. Statistical model based prediction is affected by three main sources of errors. The pre-processing of the training data, in particular the establishment of anatomical correspondences between the different samples, and the limited number of training elements constitute a first source of uncertainties. Second, the predictors can be affected by measurement noise, which will then propagate through the prediction process. Finally, and this is particularly important in the context of sparse observation data, the limited correlations between the predictors and the shape to predict imply theoretical limits for the achievable accuracy of such approaches. We have developed a framework enabling to account for these various sources of uncertainty, and propagating them through the prediction pipeline to generate confidence regions around the predicted shape. It relies extensively on cross-validation experiments in order to quantify the limitations of the statistical model with respect to the representation of new shapes (generalization ability) and to their prediction from partial data. Furthermore, we demonstrate the reliability of the obtained regions, following the procedure initially proposed in. We evaluate our approaches on a database of 140 femur bones, age range: 23–83, mean 62.57, stdv 15; 46% males and 54% females, with known age, height and weight. Morphometric measurements such as bone length, inter-condyle distance or anteversion angle are considered, either as predictors, together with sparse point clouds around the femoral head and greater trochanter, or as a pose-independent quality-of-fit metric. Cross-validation experiments indicate that a higher accuracy can be reached when complementing surface-based predictors with relevant anthropometric and morphometric information, and that reliable confidence regions can be estimated

Background. The knee joint morphology varies according to gender and morphotype of the patients. Objectives. To measure the dimensions of the proximal tibia and distal femur of osteoarthritic knees in a group of patients from the same ethnic group (Arabs) and to compare these measurements with the dimensions of six total knee implants. Patients and methods. Three-dimensional CT reconstructions were used to collect morphologic data from 124 osteoarthritic knees. Anteroposterior and mediolateral measurements were obtained from tibial and femoral bony resection surfaces planned for patient-specific instrumentation (Figures 1 and 2). These measurements were compared to the dimensions for six different types of knee implants. Results. The average tibial mediolateral (tML) and tibial anteroposterior (tAP) measurement for the study group were 74.36±6 mm and 48.94±4.57 mm, respectively; the medial tibial plateau was larger than lateral. The average femur mediolateral (fML) and femur anteroposterior (fAP) measurements for the same group were 72.04±6.6 and 68.1±7.75, respectively. For implant matching, the average tibial aspect ratio was 152.62±12.66 and the femoral average aspect ratio was 106.37±14.34. Differences were found between morphometric measurements of males and females with significantly higher parameters for males when compared to female when compared in AP and mediolateral dimensions. Also, 22.5% of the operated knees had mismatch within 2 size of the same implant. Conclusion. There is significant asymmetry of proximal tibial plateau and femur condyles. Our data suggest mismatch between osteoarthritic Arabian knees and implant designs. These ethnic differences should be considered when designing knee implants

Rapid prototyping (RP), especially useful in surgical specialities involving critical three-dimensional relationships, has recently become cheaper to access both in terms of file processing and commercially available printing resources. One potential problem has been the accuracy of models generated. We performed computed tomography on a cadaveric human patella followed by data conversion using open source software through to selective-laser-sintering of a polyamide model, to allow comparative morphometric measurements (bone v. model) using vernier calipers. Statistical testing was with Student's t-test. No significant differences in the dimensional measurements could be demonstrated. These data provide us with optimism as to the accuracy of the technology, and the feasibility of using RP cheaply to generate appropriate models for operative rehearsal of intricate orthopaedic procedures

Quantitative knowledge on the anatomy of the medial collateral ligament (MCL) is important for preventing MCL damage during unicompartmental knee arthroplasty (UKA). The objective of this study was to quantitatively determine the morphology of the medial capsule and deep MCL on tibias. METHODS. 24 cadaveric human knees (control: 19, OA: 5) were dissected to investigate the deep MCL and capsule anatomy. The specimens were fixed in full extension and this position was maintained during the dissection and morphometric measurements. The distance from the tibial insertion sites of the medial capsule including deep MCL to the medial joint surface were measured at anterior, middle, and posterior sites. Posterior capsule slope and posterior tibia slope to the anterior tibia cortex was also measured. RESULTS. In control, the distance from the tibia insertion sites of the medial capsule including deep MCL to the anterior 1/3, middle 1/3, and posterior 1/3 of medial joint surface were 12.5 ± 1.5 mm and 8.0 ± 1.6 mm and 9.4 ± 1.6 mm, respectively. Posterior capsule slope and posterior tibia slope to the anterior tibia cortex were 6.3 ± 3.3 degree and 12.7 ± 2.1 degree, respectively. In OA, the distance from the tibia insertion sites of the medial capsule including deep MCL to the anterior 1/3, middle 1/3, and posterior 1/3 of medial joint surface were 14.0 ± 1.7 mm and 9.6 ± 1.9 mm and 10.8 ± 1.5 mm, respectively. Posterior capsule slope and posterior tibia slope to the anterior tibia cortex were 8.0 ± 3.5 degree and 14.5 ± 2.2 degree, respectively. CONCLUSIONS. The morphologic data on the medial capsule and deep MCL may provide useful information for preventing MCL damage during UKA surgical procedure

Background:. Currently, there are a variety of different reverse shoulder implant designs but few anatomic studies to support the optimal selection of prosthetic size. This study analyzed the glenohumeral relationships of patients who underwent reverse shoulder arthroplasty (RSA). Methods:. Ninety-two shoulders of patients undergoing primary RSA for a massive rotator cuff tear without bony deformity or deficiency and 10 shoulders of healthy volunteers (controls) were evaluated using three-dimensional CT reconstructions and computer aided design (CAD) software. Anatomic landmarks were used to define scapular and humeral planes in addition to articular centers. After aligning the humeral center of rotation with the glenoid center, multiple glenohumeral relationships were measured and evaluated for linearity and size stratification. The correction required to transform the shoulder from its existing state (CT scan) to a realigned image (CAD model) was compared between the RSA and control groups. Size stratification was verified for statistical significance between groups. Generalized linear modeling was used to investigate if glenoid height, coronal humeral head diameter and gender were predictive of greater tuberosity positions. Results:. All 92 shoulders were grouped into three different categories based on glenoid height. The humeral head size, glenoid size, lateral offset, and inferior offset all increased linearly (r. 2. > 0.95), but the rate of increase varied (slopes range from 0.59 to 1.9). Translations required to normalize the shoulder joint were similar between healthy and pathologic cases except for superior migration. Glenoid height, coronal humeral head diameter and gender predicted the greater tuberosity position within 1.09 ± 0.84 mm of actual position in ninety percent of the patient population. Morphometric measurements for each stratified group were all found to be statistically significant between groups (p ≥ 0.05). Conclusion:. Patients who undergo RSA with minimal bony deformity have superior subluxation of the glenohumeral joint. Predicting the anatomic position of the greater tuberosity is dependent on gender, glenoid height and coronal humeral head diameter. This anatomic data provides a guide to avoid inadvertent mismatch of prosthetic and patient shoulder size. If the surgeon is able to measure glenoid height and coronal humeral head diameter preoperatively, accurate planning of the position of the greater tuberosity can be accomplished