A Novel Method for the Approximation of Humeral Head Retrotorsion Based on Three-Dimensional Registration of the Bicipital Groove

Automatic bicipital groove identification in arthritic humeri for preoperative planning: A Random Forest Classifier approach

The bicipital groove is an important anatomical feature of the proximal humerus that needs to be identified during surgical planning for procedures such as shoulder arthroplasty and proximal humeral fracture reconstruction. Current algorithms for automatic identification prove ineffective in arthritic humeri due to the presence of osteophytes, reducing their usefulness for total shoulder arthroplasty. Our methodology involves the use of a Random Forest Classifier (RFC) to automatically detect the bicipital groove on segmented computed tomography scans of humeri. We evaluated our model on two distinct test datasets: one comprising non-arthritic humeri and another with arthritic humeri characterized by significant osteophytes. Our model detected the bicipital groove with a mean absolute error of less than 1mm on arthritic humeri, demonstrating a significant improvement over the previous gold standard approach. Successful identification of the bicipital groove with a high degree of accuracy even in arthritic humeri was accomplished. This model is open source and included in the python package shoulder.

A surface registration-based approach for assessment of 3D angles in guided growth interventions in the growing femur

Purpose

Postoperative assessment of surgical interventions for correcting femoral rotational deformities necessitates a comparative analysis of femoral rotation pre- and post-surgery. While 2D assessment methods are commonly employed, ongoing debate surrounds their accuracy and reliability. To address the limitations associated with 2D analysis, we introduced and validated a 3D model-based analysis method for quantifying the angular and rotational impact of corrective rotational osteotomy in the growing femur.

Methods

The method is based on surface registration of the pre- and post-intervention 3D femoral models. To this end, 3D triangulated surface models were generated using CT images for the right femurs of 11 skeletally immature pigs, each scanned at two distinct time points with a 12-week interval between scans. In our validation procedures, femoral corrective rotational osteotomy of the post-12-week femur was simulated at varying angles of 5, 10, 15 and 20 degrees in three dimensions. Subsequently, a surface 3D/3D registration-based approach was applied to determine the 3D femoral angulation and rotation between the two models to assess the method's detection accuracy of the predefined twist angles as ground truth references.

Results

The results document the precision and accuracy of the registration-based method in evaluating rotation angles. Consistently high accuracy was observed across all angles, with an accuracy rate of 92.97% and a coefficient of variance of 8.14%.

Conclusion

This study has showcased the potential for improving post-operative assessments with significant implications for experimental studies evaluating the effects of correcting rotational deformities in the growing femur.

Level of Evidence

Not applicable.

A novel approach for joint line restoration in revision total ankle arthroplasty based on the three-dimensional registration of the contralateral tibia and fibula

PURPOSE

The use of total ankle arthroplasty (TAA) is increasing over time, as so will the need for revision TAAs in the future. Restoration of the ankle joint line (JL) in revision TAA is often difficult due to severe bone loss. This study analyzed the accuracy of a three-dimensional (3D) registration of the contralateral tibia and fibula to restore the ankle joint line (JL) and reported side-to-side differences of anatomical landmarks.

METHODS

3D triangular surface models of 96 paired lower legs underwent a surface registration algorithm for superimposition of the mirrored contralateral lower leg onto the original lower leg to approximate the original ankle JL using a proximal, middle and distal segment. Distances of the distal fibular tip, anterior and posterior medial colliculus to the JL were measured and absolute side-to-side differences reported. Anterior lateral distal tibial angle (ADTA) and lateral distal tibial angle (LDTA) were measured.

RESULTS

Mean JL approximation was most accurate for the distal segment (0.1 ± 1.4 mm (range: -3.4 to 2.8 mm)) and middle segment (0.1 ± 1.2 mm (range: -2.8 to 2.5 mm)) compared to the proximal segment (-0.2 ± 1.6 mm (range: -3.0 to 4.9 mm)) (p = 0.007). Distance of the distal fibular tip, the anterior, and posterior medial colliculus to the JL, ADTA and LDTA yielded no significant side-to-side differences (n.s.).

CONCLUSION

3D registration of the contralateral tibia and fibula reliably approximated the original ankle JL. The contralateral distal fibular tip, anterior and posterior medial colliculi, ADTA and LDTA can be used reliably for the planning of revision TAA with small side-to-side differences reported.

LEVEL OF EVIDENCE

IV.

Registration based assessment of femoral torsion for rotational osteotomies based on the contralateral anatomy

Background

Computer-assisted techniques for surgical treatment of femoral deformities have become increasingly important. In state-of-the-art 3D deformity assessments, the contralateral side is used as template for correction as it commonly represents normal anatomy. Contributing to this, an iterative closest point (ICP) algorithm is used for registration. However, the anatomical sections of the femur with idiosyncratic features, which allow for a consistent deformity assessment with ICP algorithms being unknown. Furthermore, if there is a side-to-side difference, this is not considered in error quantification.

The aim of this study was to analyze the influence and value of the different sections of the femur in 3D assessment of femoral deformities based on the contralateral anatomy.

Material and methods

3D triangular surface models were created from CT of 100 paired femurs (50 cadavers) without pathological anatomy. The femurs were divided into sections of eponymous anatomy of a predefined percentage of the whole femoral length. A surface registration algorithm was applied to superimpose the ipsilateral on the contralateral side. We evaluated 3D femoral contralateral registration (FCR) errors, defined as difference in 3D rotation of the respective femoral section before and after registration to the contralateral side. To compare this method, we quantified the landmark-based femoral torsion (LB FT). This was defined as the intra-individual difference in overall femoral torsion using with a landmark-based method.

Results

Contralateral rotational deviation ranged from 0° to 9.3° of the assessed femoral sections, depending on the section. Among the sections, the FCR error using the proximal diaphyseal area for registration was larger than any other sectional error. A combination of the lesser trochanter and the proximal diaphyseal area showed the smallest error. The LB FT error was significantly larger than any sectional error (p < 0.001).

Conclusion

We demonstrated that if the contralateral femur is used as reconstruction template, the built-in errors with the registration-based approach are smaller than the intraindividual difference of the femoral torsion between both sides. The errors are depending on the section and their idiosyncratic features used for registration. For rotational osteotomies a combination of the lesser trochanter and the proximal diaphyseal area sections seems to allow for a reconstruction with a minimal error.

Hybrid curvature-geometrical detection of landmarks for the automatic analysis of the reduction of supracondylar fractures of the femur

BACKGROUND AND OBJECTIVE

The analysis of the features of certain tissues is required by many procedures of modern medicine, allowing the development of more efficient treatments. The recognition of landmarks allows the planning of orthopedic and trauma surgical procedures, such as the design of prostheses or the treatment of fractures. Formerly, their detection has been carried out by hand, making the workflow inaccurate and tedious. In this paper we propose an automatic algorithm for the detection of landmarks of human femurs and an analysis of the quality of the reduction of supracondylar fractures.

METHODS

The detection of anatomical landmarks follows a knowledge-based approach, consisting of a hybrid strategy: curvature and spatial decomposition. Prior training is unrequired. The analysis of the reduction quality is performed by a side-to-side comparison between healthy and fractured sides. The pre-clinical validation of the technique consists of a two-stage study: Initially, we tested our algorithm with 14 healthy femurs, comparing the output with ground truth values. Then, a total of 140 virtual fractures was processed to assess the validity of our analysis of the quality of reduction. A two-sample t test and correlation coefficients between metrics and the degree of reduction have been employed to determine the reliability of the algorithm.

RESULTS

The average detection error of landmarks was maintained below 1.7 mm and 2^∘ (p< 0.01) for points and axes, respectively. Regarding the contralateral analysis, the resulting P-values reveal the possibility to determine whether a supracondylar fracture is properly reduced or not with a 95% of confidence. Furthermore, the correlation is high between the metrics and the quality of the reduction.

CONCLUSIONS

This research concludes that our technique allows to classify supracondylar fracture reductions of the femur by only analyzing the detected anatomical landmarks. A initial training set is not required as input of our algorithm.

Restoration of the patient-specific anatomy of the distal fibula based on a novel three-dimensional contralateral registration method

Purpose

Posttraumatic fibular malunion alters ankle joint biomechanics and may lead to pain, stiffness, and premature osteoarthritis. The accurate restoration is key for success of reconstructive surgeries. The aim of this study was to analyze the accuracy of a novel three-dimensional (3D) registration algorithm using different segments of the contralateral anatomy to restore the distal fibula.

Methods

Triangular 3D surface models were reconstructed from computed tomographic data of 96 paired lower legs. Four segments were defined: 25% tibia, 50% tibia, 75% fibula, and 75% fibula and tibia. A surface registration algorithm was used to superimpose the mirrored contralateral model on the original model. The accuracy of distal fibula restoration was measured.

Results

The median rotation error, 3D distance (Euclidean distance), and 3D angle (Euler’s angle) using the distal 25% tibia segment for the registration were 0.8° (− 1.7–4.8), 2.1 mm (1.4–2.9), and 2.9° (1.9–5.4), respectively. The restoration showed the highest errors using the 75% fibula segment (rotation error 3.2° (0.1–8.3); Euclidean distance 4.2 mm (3.1–5.8); Euler’s angle 5.8° (3.4–9.2)). The translation error did not differ significantly between segments.

Conclusion

3D registration of the contralateral tibia and fibula reliably approximated the premorbid anatomy of the distal fibula. Registration of the 25% distal tibia, including distinct anatomical landmarks of the fibular notch and malleolar colliculi, restored the anatomy with increasing accuracy, minimizing both rotational and translational errors. This new method of evaluating malreductions could reduce morbidity in patients with ankle fractures.

Level of evidence

IV

Accuracy of joint line restoration based on three-dimensional registration of the contralateral tibial tuberosity and the fibular tip

Purpose

To assess a novel method of three-dimensional (3D) joint line (JL) restoration based on the contralateral tibia and fibula.

Methods

3D triangular surface models were generated from computed tomographic data of 96 paired lower legs (48 cadavers) without signs of pathology. Three segments of the tibia and fibula, excluding the tibia plateau, were defined (tibia, fibula, tibial tuberosity (TT) and fibular tip). A surface registration algorithm was used to superimpose the mirrored contralateral model onto the original model. JL approximation and absolute mean errors for each segment registration were measured and its relationship to gender, height, weight and tibia and fibula length side-to-side differences analyzed. Fibular tip to JL distance was measured and analyzed.

Results

Mean JL approximation did not yield significant differences among the three segments. Mean absolute JL error was highest for the tibia 1.4 ± 1.4 mm (range: 0 to 6.0 mm) and decreased for the fibula 0.8 ± 1.0 mm (range: 0 to 3.7 mm) and for TT and fibular tip segment 0.7 ± 0.6 (range: 0 to 2.4 mm) (p = 0.03). Mean absolute JL error of the TT and fibular tip segment was independent of gender, height, weight and tibia and fibula length side-to-side differences. Mean fibular tip to JL distance was 11.9 ± 3.4 mm (range: 3.4 to 22.1 mm) with a mean absolute side-to-side difference of 1.6 ± 1.1 mm (range: 0 to 5.3 mm).

Conclusion

3D registration of the contralateral tibia and fibula reliably approximated the original JL. The registration of, TT and fibular tip, as robust anatomical landmarks, improved the accuracy of JL restoration independent of tibia and fibula length side-to-side differences.

Level of evidence

IV

Does computerized CT-based 3D planning of the humeral head cut help to restore the anatomy of the proximal humerus after stemless total shoulder arthroplasty?

BACKGROUND

Restoration of proximal humeral anatomy (RPHA) after total shoulder arthroplasty (TSA) has been shown to result in better clinical outcomes than is the case in nonanatomic humeral reconstruction. Preoperative virtual planning has mainly focused on glenoid component placement. Such planning also has the potential to improve anatomic positioning of the humeral head by more accurately guiding the humeral head cut and aid in the selection of anatomic humeral component sizing. It was hypothesized that the use of preoperative 3-dimensional (3D) planning helps to reliably achieve RPHA after stemless TSA.

METHODS

One hundred consecutive stemless TSA (67 males, 51 right shoulder, mean age of 62 ±9.4 years) were radiographically assessed using pre- and postoperative standardized anteroposterior radiographs. The RPHA was measured with the so-called circle method described by Youderian et al. We measured deviation from the premorbid center of rotation (COR), and more than 3 mm was considered as minimal clinically important difference. Additionally, pre- and postoperative humeral head diameter (HHD), head-neck angle (HNA), and humeral head height (HHH) were measured to assess additional geometrical risk factors for poor RPHA.

RESULTS

The mean distance from of the premorbid to the implanted head COR was 4.3 ± 3.1 mm. Thirty-five shoulders (35%) showed a deviation of less than 3 mm (mean 1.9 ±1.1) and 65 shoulders (65%) a deviation of ≥3 mm (mean 8.0 ± 3.7). Overstuffing was the main reason for poor RPHA (88%). The level of the humeral head cut was responsible for overstuffing in 46 of the 57 overstuffed cases. The preoperative HHD, HHH, and HNA were significantly larger, higher, and more in valgus angulation in the group with accurate RPHA compared with the group with poor RPHA (HHD of 61.1 mm ± 4.4 vs. 55.9 ± 6.6, P < .001; HHH 8.6±2.2 vs. 7.6±2.6, P = .026; and varus angulation of 134.7° ±6.4° vs. 131.0° ±7.91, P = .010).

CONCLUSION

Restoration of proximal humeral anatomy after stemless TSA using computed tomography (CT)-based 3D planning was not precise. A poorly performed humeral head cut was the main reason for overstuffing, which was seen in 88% of the cases with inaccurate RPHA. Preoperative small HHD, low HHH, and varus-angulated HNA are risk factors for poor RPHA after stemless TSA.

Automatic detection of landmarks for the analysis of a reduction of supracondylar fractures of the humerus

An accurate identification of bone features is required by modern orthopedics to improve patient recovery. The analysis of landmarks enables the planning of a fracture reduction surgery, designing prostheses or fixation devices, and showing deformities accurately. The recognition of these features was previously performed manually. However, this long and tedious process provided insufficient accuracy. In this paper, we propose a geometrically-based algorithm that automatically detects the most significant landmarks of a humerus. By employing contralateral images of the upper limb, a side-to-side study of the landmarks is also conducted to analyze the goodness of supracondylar fracture reductions. We conclude that a reduction can be classified by only considering the detected landmarks. In addition, our technique does not require a prior training, thus becoming a reliable alternative to treat this kind of fractures.