Automated bone segmentation from large field of view 3D MR images of the hip joint

Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH

BACKGROUND

Developmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center-edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies.

METHODS

This study employed a U-Net-based CNN model to automate the segmentation of hip MRI images in children. The segmentation process was validated using a leave-one-out method during training. Subsequently, the segmented hip joint images were utilized in clinical settings to perform automated measurements of key angles: AI, femoral neck angle (FNA), and CEA. This automated approach aimed to replace manual measurements and provide an objective reference for clinical assessments.

RESULTS

The U-Net-based network demonstrates high effectiveness in hip segmentation compared to manual radiologist segmentations. In test data, it achieves average DSC values of 0.9109 (acetabulum) and 0.9244 (proximal femur), with a 91.76% segmentation success rate. The average ASD values are 0.3160 mm (acetabulum) and 0.6395 mm (proximal femur) in test data, with Ground Truth (GT) edge points and predicted segmentation maps having a mean distance of less than 1 mm. Using automated segmentation models for clinical hip angle measurements (CEA, AI, FNA) shows no statistical difference compared to manual measurements (p > 0.05).

CONCLUSION

Utilizing U-Net-based image segmentation and automated measurement of morphological parameters significantly enhances the accuracy and efficiency of DDH assessment. These methods improve precision in automatic measurements and provide an objective basis for clinical diagnosis and treatment of DDH.

SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images

Deep Learning (DL) techniques have recently been used in medical image segmentation and the reconstruction of 3D anatomies of a human body. In this work, we propose a semi-supervised DL (SSDL) approach utilizing a CNN-based 3D U-Net model for femur segmentation from sparsely annotated quantitative computed tomography (QCT) slices. Specifically, QCT slices at the proximal end of the femur forming ball and socket joint with acetabulum were annotated for precise segmentation, where a segmenting binary mask was generated using a 3D U-Net model to segment the femur accurately. A total of 5474 QCT slices were considered for training among which 2316 slices were annotated. 3D femurs were further reconstructed from segmented slices employing polynomial spline interpolation. Both qualitative and quantitative performance of segmentation and 3D reconstruction were satisfactory with more than 90% accuracy achieved for all of the standard performance metrics considered. The spatial overlap index and reproducibility validation metric for segmentation-Dice Similarity Coefficient was 91.8% for unseen patients and 99.2% for validated patients. An average relative error of 12.02% and 10.75% for volume and surface area, respectively, were computed for 3D reconstructed femurs. The proposed approach demonstrates its effectiveness in accurately segmenting and reconstructing 3D femur from QCT slices.

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification

Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landmark-based detection and quantification of hip abnormalities from magnetic resonance (MR) images. The framework relies on a novel idea of multi-landmark environment analysis with reinforcement learning. In particular, we merge the concepts of the graphical lasso and Morris sensitivity analysis with deep neural networks to quantitatively estimate the contribution of individual landmark and landmark subgroup locations to the other landmark locations. Convolutional neural networks for image segmentation are utilized to propose the initial landmark locations, and landmark detection is then formulated as a reinforcement learning (RL) problem, where each landmark-agent can adjust its position by observing the local MR image neighborhood and the locations of the most-contributive landmarks. The framework was validated on T1-, T2- and proton density-weighted MR images of 260 patients with the aim to measure the lateral center-edge angle (LCEA), femoral neck-shaft angle (NSA), and the anterior and posterior acetabular sector angles (AASA and PASA) of the hip, and derive the quantitative abnormality metrics from these angles. The framework was successfully tested using the UNet and feature pyramid network (FPN) segmentation architectures for landmark proposal generation, and the deep Q-network (DeepQN), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and actor-critic policy gradient (A2C) RL networks for landmark position optimization. The resulting overall landmark detection error of 1.5 mm and angle measurement error of 1.4° indicates a superior performance in comparison to existing methods. Moreover, the automatically estimated abnormality labels were in 95% agreement with those generated by an expert radiologist.

Multimodality-based knee joint modelling method with bone and cartilage structures for total knee arthroplasty

OBJECTIVE

We propose a robust and accurate knee joint modelling method with bone and cartilage structures to enable accurate surgical guidance for knee surgery.

METHODS

A multimodality registration strategy is proposed to fuse magnetic resonance (MR) and computed tomography (CT) images of the femur and tibia separately to remove spatial inconsistency caused by knee bending in CT/MR scans. Automatic segmentation of the femur, tibia and cartilages is carried out with region of interest clustering and intensity analysis based on the multimodal fusion of images.

RESULTS

Experimental results show that the registration error is 1.13 ± 0.30 mm. The Dice similarity coefficient values of the proposed segmentation method of the femur, tibia, femoral and tibial cartilages are 0.969, 0.966, 0.910 and 0.872, respectively.

CONCLUSIONS

This study demonstrates the feasibility and effectiveness of multimodality-based registration and segmentation methods for knee joint modelling. The proposed method can provide users with 3D anatomical models of the femur, tibia, and cartilages with few human inputs.

Automated analysis of immediate reliability of T2 and T2* relaxation times of hip joint cartilage from 3 T MR examinations

BACKGROUND

Magnetic resonance (MR) T2 and T2* mapping sequences allow in vivo quantification of biochemical characteristics within joint cartilage of relevance to clinical assessment of conditions such as hip osteoarthritis (OA).

PURPOSE

To evaluate an automated immediate reliability analysis of T2 and T2* mapping from MR examinations of hip joint cartilage using a bone and cartilage segmentation pipeline based around focused shape modelling.

STUDY TYPE

Technical validation.

SUBJECTS

17 asymptomatic volunteers (M: F 7:10, aged 22-47 years, mass 50-90 kg, height 163-189 cm) underwent unilateral hip joint MR examinations. Automated analysis of cartilage T2 and T2* data immediate reliability was evaluated in 9 subjects (M: F 4: 5) for each sequence.

FIELD STRENGTH/SEQUENCE

A 3 T MR system with a body matrix flex-coil was used to acquire images with the following sequences: T2 weighted 3D-trueFast Imaging with Steady-State Precession (water excitation; 10.18 ms repetition time (TR); 4.3 ms echo time (TE); Voxel Size (VS): 0.625 × 0.625 × 0.65 mm; 160 mm field of view (FOV); Flip Angle (FA): 30 degrees; Pixel Bandwidth (PB): 140 Hz/pixel); a multi-echo spin echo (MESE) T2 mapping sequence (TR/TE: 2080/18-90 ms (5 echoes); VS: 4 × 0.78 × 0.78 mm; FOV: 200 mm; FA: 180 degrees; PB: 230 Hz/pixel) and a MESE T2* mapping sequence (TR/TE: 873/3.82-19.1 ms (5 echoes); VS: 3 × 0.625 × 0.625 mm; FOV: 160 mm; FA: 25 degrees; PB: 250 Hz/pixel).

ASSESSMENT

Automated cartilage segmentation and quantitative analysis provided T2 and T2* data from test-retest MR examinations to assess immediate reliability.

STATISTICAL TESTS

Coefficient of variation (CV) and intraclass correlations (ICC_{2, 1}) to analyse automated T2 and T2* mapping reliability focusing on the clinically important superior cartilage regions of the hip joint.

RESULTS

Comparisons between test-retest T2 and (T2*) data revealed mean CV's of 3.385% (1.25%), mean ICC_{2, 1}'s of 0.871 (0.984) and median mean differences of -1.139ms (+0.195ms).

CONCLUSION

The T2 and T2* times from automated analyses of hip cartilage from test-retest MR examinations had high (T2) and excellent (T2*) immediate reliability.

ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.

Discrete element and finite element methods provide similar estimations for hip joint contact mechanics during walking gait

Finite element analysis (FEA) provides a powerful approach for estimating the in-vivo loading characteristics of the hip joint during various locomotory and functional activities. However, time-consuming procedures, such as the generation of high-quality FE meshes and setup of FE simulation, typically make the method impractical for rapid applications which could be used in clinical routine. Alternatively, discrete element analysis (DEA) has been developed to quantify mechanical conditions of the hip joint in a fraction of time compared to FEA. Although DEA has proven effective in the estimation of contact stresses and areas in various complex applications, it has not yet been well characterised by its ability to evaluate contact mechanics for the hip joint during gait cycle loading using data from several individuals. The objective of this work was to compare DEA modelling against well-established FEA for analysing contact mechanics of the hip joint during walking gait. Subject-specific models were generated from magnetic resonance images of the hip joints in five asymptomatic subjects. The DEA and FEA models were then simulated for 13 loading time-points extracted from a full gait cycle. Computationally, DEA was substantially more efficient compared to FEA (simulation times of seconds vs. hours). The DEA and FEA methods had similar predictions for contact pressure distribution for the hip joint during normal walking. In all 13 simulated loading time-points across five subjects, the maximum difference in average contact pressures between DEA and FEA was within ±0.06 MPa. Furthermore, the difference in contact area ratio computed using DEA and FEA was less than ±6%.

The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs

Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more reliable analysis of large datasets and to enable further downstream processing e.g. automated diagnosis. In this work, we evaluate the use of conditional Generative Adversarial Networks (cGANs) as a robust and potentially improved method for semantic segmentation compared to other extensively used convolutional neural network, such as the U-Net. As cGANs have not yet been widely explored for semantic medical image segmentation, we analysed the effect of training with different objective functions and discriminator receptive field sizes on the segmentation performance of the cGAN. Additionally, we evaluated the possibility of using transfer learning to improve the segmentation accuracy. The networks were trained on i) the SKI10 dataset which comes from the MICCAI grand challenge "Segmentation of Knee Images 2010″, ii) the OAI ZIB dataset containing femoral and tibial bone and cartilage segmentations of the Osteoarthritis Initiative cohort and iii) a small locally acquired dataset (Advanced MRI of Osteoarthritis (AMROA) study) consisting of 3D fat-saturated spoiled gradient recalled-echo knee MRIs with manual segmentations of the femoral, tibial and patellar bone and cartilage, as well as the cruciate ligaments and selected peri-articular muscles. The Sørensen-Dice Similarity Coefficient (DSC), volumetric overlap error (VOE) and average surface distance (ASD) were calculated for segmentation performance evaluation. DSC ≥ 0.95 were achieved for all segmented bone structures, DSC ≥ 0.83 for cartilage and muscle tissues and DSC of ≈0.66 were achieved for cruciate ligament segmentations with both cGAN and U-Net on the in-house AMROA dataset. Reducing the receptive field size of the cGAN discriminator network improved the networks segmentation performance and resulted in segmentation accuracies equivalent to those of the U-Net. Pretraining not only increased segmentation accuracy of a few knee joint tissues of the fine-tuned dataset, but also increased the network's capacity to preserve segmentation capabilities for the pretrained dataset. cGAN machine learning can generate automated semantic maps of multiple tissues within the knee joint which could increase the accuracy and efficiency for evaluating joint health.

Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols

Medical image segmentation is one of the most crucial issues in medical image processing and analysis. In general, segmentation of the various structures in medical images is performed for the further image analyzes such as quantification, assessment, diagnosis, prognosis and classification. In this paper, a research study for the 2D semantic segmentation of the multiform, both spheric and aspheric, femoral head and proximal femur bones in magnetic resonance imaging (MRI) sections of the patients with Legg-Calve-Perthes disease (LCPD) with the deep convolutional neural networks (CNNs) is presented. In the scope of the proposed study, bilateral hip MRI sections acquired in coronal plane were used. The main characteristic of the MRI sections that were used is to be low quality images which were obtained in different MRI protocols by using 3 different MRI scanners with 1.5 T imaging capability. In performance evaluations, promising segmentation results were achieved with deep CNNs in low quality MRI sections acquired in different MRI protocols. A success rate about 90% was observed in semantic segmentation of the multiform femoral head and proximal femur bones in a total of 194 MRI sections obtained from 33 MRI sequences of 13 patients with deep CNNs.

Prediction of pelvic tumour coverage by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) from referral imaging

BACKGROUND

Patient suitability for magnetic resonance-guided high intensity focused ultrasound (MRgHIFU) ablation of pelvic tumors is initially evaluated clinically for treatment feasibility using referral images, acquired using standard supine diagnostic imaging, followed by MR screening of potential patients lying on the MRgHIFU couch in a 'best-guess' treatment position. Existing evaluation methods result in ≥40% of referred patients being screened out because of tumor non-targetability. We hypothesize that this process could be improved by development of a novel algorithm for predicting tumor coverage from referral imaging.

METHODS

The algorithm was developed from volunteer images and tested with patient data. MR images were acquired for five healthy volunteers and five patients with recurrent gynaecological cancer. Subjects were MR imaged supine and in oblique-supine-decubitus MRgHIFU treatment positions. Body outline and bones were segmented for all subjects, with organs-at-risk and tumors also segmented for patients. Supine images were aligned with treatment images to simulate a treatment dataset. Target coverage (of patient tumors and volunteer intra-pelvic soft tissue), i.e. the volume reachable by the MRgHIFU focus, was quantified. Target coverage predicted from supine imaging was compared to that from treatment imaging.

RESULTS

Mean (±standard deviation) absolute difference between supine-predicted and treatment-predicted coverage for 5 volunteers was 9 ± 6% (range: 2-22%) and for 4 patients, was 12 ± 7% (range: 4-21%), excluding a patient with poor acoustic coupling (coverage difference was 53%).

CONCLUSION

Prediction of MRgHIFU target coverage from referral imaging appears feasible, facilitating further development of automated evaluation of patient suitability for MRgHIFU.

Automated postoperative muscle assessment of hip arthroplasty patients using multimodal imaging joint segmentation

BACKGROUND AND OBJECTIVE

In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition and assess implant failure. In this work, we combine CT and MRI for joint bone and muscle segmentation and we propose a novel Intramuscular Fat Fraction estimation method for the quantification of muscle atrophy.

METHODS

Our multimodal framework is able to segment healthy and pathological musculoskeletal structures as well as implants, and develops into three steps. First, input images are pre-processed to improve the low quality of clinically acquired images and to reduce the noise associated with metal artefact. Subsequently, CT and MRI are non-linearly aligned using a novel approach which imposes rigidity constraints on bony structures to ensure realistic deformation. Finally, taking advantage of a multimodal atlas we created for this task, a multi-atlas based segmentation delineates pelvic bones, abductor muscles and implants on both modalities jointly. From the obtained segmentation, a multimodal estimation of the Intramuscular Fat Fraction can be automatically derived.

RESULTS

Evaluation of the segmentation in a leave-one-out cross-validation study on 22 hip sides resulted in an average Dice score of 0.90 for skeletal and 0.84 for muscular structures. Our multimodal Intramuscular Fat Fraction was benchmarked on 27 different cases against a standard radiological score, showing stronger association than a single modality approach in a one-way ANOVA F-test analysis.

CONCLUSIONS

The proposed framework represents a promising tool to support image analysis in hip arthroplasty, being robust to the presence of implants and associated image artefacts. By allowing for the automated extraction of a muscle atrophy imaging biomarker, it could quantitatively inform the decision-making process about patient's management.

A principal component analysis-based framework for statistical modeling of bone displacement during wrist maneuvers

We present a method for the statistical modeling of the displacements of wrist bones during the performance of coordinated maneuvers, such as radial-ulnar deviation (RUD). In our approach, we decompose bone displacement via a set of basis functions, identified via principal component analysis (PCA). We utilized MRI wrist scans acquired at multiple static positions for deriving these basis functions. We then utilized these basis functions to compare the displacements undergone by the bones of the left versus right wrist in the same individual, and between bones of the wrists of men and women, during the performance of the coordinated RUD maneuver. Our results show that the complex displacements of the wrist bones during RUD can be modeled with high reliability with just 5 basis functions, that captured over 91% of variation across individuals. The basis functions were able to predict intermediate wrist bone poses with an overall high accuracy (mean error of 0.26 mm). Our proposed approach found statistically significant differences between bone displacement trajectories in women versus men, however, did not find significant differences in those of the left versus right wrist in the same individual. Our proposed method has the potential to enable detailed analysis of wrist kinematics for each sex, and provide a robust framework for characterizing the normal and pathologic displacement of the wrist bones, such as in the context of wrist instability.

[Femoroacetabular impingement - Update 2019]

BACKGROUND

Since the first description of the femoroacetabular impingement (FAI) concept diagnostic imaging of FAI has continuously been developed.

OBJECTIVE

The biomechanical concept is explained and an update on diagnostic imaging of FAI is presented.

MATERIAL AND METHODS

Based on a literature search this review article presents the current state of knowledge about FAI mechanisms and gives an overview on state of the art radiological diagnostics. A perspective on new imaging methods is also given.

RESULTS

The FAI is a dynamic phenomenon with a mechanical conflict between the femoral head and/or neck and the acetabulum. It is usually suspected clinically; however, imaging plays an essential role in establishing the diagnosis by detecting and defining the underlying deformities of the proximal femur (cam deformity) and the acetabulum (pincer deformity) and by evaluating associated lesions of the articular cartilage and labrum. Basic imaging diagnostics consist of anteroposterior and lateral radiographs. Magnetic resonance imaging (MRI) and MR arthrography are the preferred imaging modalities for detailed analysis of deformities, for the detection and graduation of lesions of articular cartilage (sensitivity 58-91%) and labral lesions (sensitivity 50-92%). Simultaneously, these methods can exclude other hip diseases. Current standards and new developments in FAI imaging are presented.

CONCLUSION

For the diagnosis of FAI typical clinical and imaging findings are required. Radiological diagnostics are an indispensable component in establishing the diagnosis of FAI, in the differentiation of the underlying deformities and in the assessment of treatment-relevant joint damage.

Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration

BACKGROUND

Radial 2D MRI scans of the hip are routinely used for the diagnosis of the cam type of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, both considered causes of hip joint osteoarthritis in young and active patients. A method for automated and accurate segmentation of the proximal femur from radial MRI scans could be very useful in both clinical routine and biomechanical studies. However, to our knowledge, no such method has been published before.

PURPOSE

The aims of this study are the development of a system for the segmentation of the proximal femur from radial MRI scans and the reconstruction of its 3D model that can be used for diagnosis and planning of hip-preserving surgery.

METHODS

The proposed system relies on: (a) a random forest classifier and (b) the registration of a 3D template mesh of the femur to the radial slices based on a physically based deformable model. The input to the system are the radial slices and the manually specified positions of three landmarks. Our dataset consists of the radial MRI scans of 25 patients symptomatic of FAI or AVN and accompanying manual segmentation of the femur, treated as the ground truth.

RESULTS

The achieved segmentation of the proximal femur has an average Dice similarity coefficient (DSC) of 96.37 ± 1.55%, an average symmetric mean absolute distance (SMAD) of 0.94 ± 0.39 mm and an average Hausdorff distance of 2.37 ± 1.14 mm. In the femoral head subregion, the average SMAD is 0.64 ± 0.18 mm and the average Hausdorff distance is 1.41 ± 0.56 mm.

CONCLUSIONS

We validated a semiautomated method for the segmentation of the proximal femur from radial MR scans. A 3D model of the proximal femur is also reconstructed, which can be used for the planning of hip-preserving surgery.

Can We Discriminate Symptomatic Hip Patients From Asymptomatic Volunteers Based on Anatomic Predictors? A 3-Dimensional Magnetic Resonance Study on Cam, Pincer, and Spinopelvic Parameters

BACKGROUND

Given the high prevalence of patients with hip deformities and no ongoing hip dysfunction, understanding the anatomic factors predicting the symptomatic state is critical. One such variable is how the spinopelvic parameters (SPPs) may interplay with hip anatomic factors.

HYPOTHESIS/PURPOSE

SPPs and femoral- and acetabular-specific parameters may predict which patients will become symptomatic. The purpose was to determine which anatomic characteristics with specific cutoffs were associated with hip symptom development and how these parameters relate to each other.

STUDY DESIGN

Cohort study (Diagnosis); Level of evidence, 2.

METHODS

548 participants were designated either symptomatic patients (n = 176, scheduled for surgery with hip pain and/or functional limitation) or asymptomatic volunteers (n = 372, no pain) and underwent 3-dimensional magnetic resonance imaging. Multiple femoral (α angle, Ω angle, neck angle, torsion), acetabular (version, coverage), and spinopelvic (pelvic tilt, sacral slope [SS], pelvic incidence) parameters were measured semiautomatically. Normative values, optimal differentiating thresholds, and a logistic regression analysis were computed.

RESULTS

Symptomatic patients had larger cam deformities (defined by increased Ω angle and α angle), smaller acetabular coverage, and larger pelvic incidence and SS angles compared with the asymptomatic volunteers. Discriminant receiver operating characteristic analysis confirmed that radial 2-o'clock α angle (threshold 58°-60°, sensitivity 75%-60%, specificity 80%-84%; area under the curve [AUC] = 0.831), Ω angle (threshold 43°, sensitivity 72%, specificity 70%; AUC = 0.830), acetabular inclination (threshold 6°, sensitivity 65%, specificity 70%; AUC = 0.709), and SS (threshold 44°, sensitivity 72%, specificity 75%; AUC = 0.801) ( P < .005) were the best parameters to classify participants. When parameters were entered into a logistic regression, significant positive predictors for the symptomatic patients were achieved for SS, acetabular inclination, Ω angle, and α angle at 2-o'clock, correctly classifying 85% of cases (model sensitivity 72%, specificity 91%; AUC = 0.919).

CONCLUSION

Complex dynamic interplay exists between the hip and SPPs. A cam deformity, acetabular undercoverage, and increased SPP angles are predictive of a hip symptomatic state. SPPs were significant to discriminate between participants and were important in combination with other hip deformities. Symptomatic patients can be effectively differentiated from asymptomatic volunteers based on predictive anatomic factors.

A lightweight rapid application development framework for biomedical image analysis

Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment. SMILI supports the development of both command-line (shell and Python scripting) and graphical applications utilising the same set of processing algorithms. It provides a substantial subset of features when compared to more complex packages, yet it is small enough to ship with clinical applications with limited overhead and has a license suitable for commercial use. After describing where SMILI fits within the existing biomedical imaging software ecosystem, by comparing it to other state-of-the-art offerings, we demonstrate its capabilities in creating a clinical application for manual measurement of cam-type lesions of the femoral head-neck region for the investigation of femoro-acetabular impingement (FAI) from three dimensional (3D) magnetic resonance (MR) images of the hip. This application for the investigation of FAI proved to be convenient for radiological analyses and resulted in high intra (ICC=0.97) and inter-observer (ICC=0.95) reliabilities for measurement of α-angles of the femoral head-neck region. We believe that SMILI is particularly well suited for prototyping biomedical imaging applications requiring user interaction and/or visualisation of 3D mesh, scalar, vector or tensor data.

Method for Segmentation of Knee Articular Cartilages Based on Contrast-Enhanced CT Images

Segmentation of contrast-enhanced computed tomography (CECT) images enables quantitative evaluation of morphology of articular cartilage as well as the significance of the lesions. Unfortunately, automatic segmentation methods for CECT images are currently lacking. Here, we introduce a semiautomated technique to segment articular cartilage from in vivo CECT images of human knee. The segmented cartilage geometries of nine knee joints, imaged using a clinical CT-scanner with an intra-articular contrast agent, were compared with manual segmentations from CT and magnetic resonance (MR) images. The Dice similarity coefficients (DSCs) between semiautomatic and manual CT segmentations were 0.79-0.83 and sensitivity and specificity values were also high (0.76-0.86). When comparing semiautomatic and manual CT segmentations, mean cartilage thicknesses agreed well (intraclass correlation coefficient = 0.85-0.93); the difference in thickness (mean ± SD) was 0.27 ± 0.03 mm. Differences in DSC, when MR segmentations were compared with manual and semiautomated CT segmentations, were statistically insignificant. Similarly, differences in volume were not statistically significant between manual and semiautomatic CT segmentations. Semiautomation decreased the segmentation time from 450 ± 190 to 42 ± 10 min per joint. The results reveal that the proposed technique is fast and reliable for segmentation of cartilage. Importantly, this is the first study presenting semiautomated segmentation of cartilage from CECT images of human knee joint with minimal user interaction.

Does 3DMR provide equivalent information as 3DCT for the pre-operative evaluation of adult Hip pain conditions of femoroacetabular impingement and Hip dysplasia?

OBJECTIVE:

Femoroacetabular impingement (FAI) and hip dysplasia (HD) are frequently evaluated by isotropic CT (3DCT) for preoperative planning at the expense of radiation. The aim was to determine if isotropic MRI (3DMR) imaging can provide similar quantitative and qualitative morphological information as 3DCT.

METHODS:

25 consecutive patients with a final diagnosis of FAI or HD were retrospectively selected from December 2016-December 2017. Two readers (R1, R2) performed quantitative angular measurements on 3DCT and 3DMR, blinded to the diagnosis and each other's measurements. 3DMR and 3DCT of the hips were qualitatively and independently evaluated by a radiologist (R3), surgeon (R4), and fellow (R5). Interobserver and intermodality comparisons were performed.

RESULTS:

The ICC was good to excellent for all measurements between R1 and R2 (ICC:0.60-0.98) and the majority of intermodality measurements for R1 and R2. Average inter-reader and inter-modality PABAK showed good to excellent agreement for qualitative reads. On CT, all alpha angles (AA) were significantly lower in dysplasia patients than in cam patients (p < 0.05). All lateral center-edge angle (LCEA) were significantly lower in dysplasia than in cam patients (p < 0.05). On MR, AA at 12, 1, and 2 o'clock, and LCEA at center were significantly lower in dysplasia patients than in cam patients (p < 0.05).

CONCLUSION:

3DMR offers similar qualitative and quantitative analysis as 3DCT in adult painful hip conditions.

ADVANCES IN KNOWLEDGE:

3DMR has good potential to replace 3DCT and serve as a one-stop modality for bone and soft tissue characterizations in the pre-operative evaluation of FAI and HD.

Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm

BACKGROUND AND OBJECTIVE

Automated segmentation with high accuracy and speed is a prerequisite for FEA-based quantitative assessment with a large population. However, hip joint segmentation has remained challenging due to a narrow articular cartilage and thin cortical bone with a marked interindividual variance. To overcome this challenge, this paper proposes a fully automated segmentation method for a hip joint that uses the complementary characteristics between the thresholding technique and the watershed algorithm.

METHODS

Using the golden section method and load path algorithm, the proposed method first determines the patient-specific optimal threshold value that enables reliably separating a femur from a pelvis while removing cortical and trabecular bone in the femur at the minimum. This provides regional information on the femur. The watershed algorithm is then used to obtain boundary information on the femur. The proximal femur can be extracted by merging the complementary information on a target image.

RESULTS

For eight CT images, compared with the manual segmentation and other segmentation methods, the proposed method offers a high accuracy in terms of the dice overlap coefficient (97.24 ± 0.44%) and average surface distance (0.36 ± 0.07 mm) within a fast timeframe in terms of processing time per slice (1.25 ± 0.27 s). The proposed method also delivers structural behavior which is close to that of the manual segmentation with a small mean of average relative errors of the risk factor (4.99%).

CONCLUSION

The segmentation results show that, without the aid of a prerequisite dataset and users' manual intervention, the proposed method can segment a hip joint as fast as the simplified Kang (SK)-based automated segmentation, while maintaining the segmentation accuracy at a similar level of the snake-based semi-automated segmentation.

Simulated radiographic bone and joint modeling from 3D ankle MRI: feasibility and comparison with radiographs and 2D MRI

PURPOSE

The purpose of this work is to simulate radiographs from isotropic 3D MRI data, compare relationship of angle and joint space measurements on simulated radiographs with corresponding 2D MRIs and real radiographs (XR), and compare measurement times among the three modalities.

MATERIALS AND METHODS

Twenty-four consecutive ankles were included, eight males and 16 females, with a mean age of 46 years. Segmented joint models simulating radiographs were created from 3D MRI data sets. Three readers independently performed blinded angle and joint space measurements on the models, corresponding 2D MRIs, and XRs at two time points. Linear mixed models and the intraclass correlation coefficient (ICC) was ascertained, with p values less than 0.05 considered significant.

RESULTS

Simulated radiograph models were successfully created in all cases. Good agreement (ICC > 0.65) was noted among all readers across all modalities and among most measurements. Absolute measurement values differed between modalities. Measurement time was significantly greater (p < 0.05) on 2D versus simulated radiographs for most measurements and on XR versus simulated radiographs (p < 0.05) for nearly half the measurements.

CONCLUSIONS

Simulated radiographs can be successfully generated from 3D MRI data; however, measurements differ. Good inter-reader and moderate-to-good intra-reader reliability was observed and measurements obtained on simulated radiograph models took significantly less time compared to measurements with 2D and generally less time than XR.

Whole-body bone segmentation from MRI for PET/MRI attenuation correction using shape-based averaging

PURPOSE

The authors evaluate the performance of shape-based averaging (SBA) technique for whole-body bone segmentation from MRI in the context of MRI-guided attenuation correction (MRAC) in hybrid PET/MRI. To enhance the performance of the SBA scheme, the authors propose to combine it with statistical atlas fusion techniques. Moreover, a fast and efficient shape comparison-based atlas selection scheme was developed and incorporated into the SBA method.

METHODS

Clinical studies consisting of PET/CT and MR images of 21 patients were used to assess the performance of the SBA method. In addition, the authors assessed the performance of simultaneous truth and performance level estimation (STAPLE) and the selective and iterative method for performance level estimation (SIMPLE) combined with SBA. In addition, a local shape comparison scheme (L-Shp) was proposed to improve the performance of SBA. The SIMPLE method was applied globally (G-SIMPLE) while STAPLE method was employed at both global (G-STAPLE) and local (L-STAPLE) levels. The evaluation was performed based on the accuracy of extracted whole-body bones, fragmentation, and computation time achieved by the different methods. The majority voting (MV) atlas fusion scheme was also evaluated as a conventional and commonly used method. MRI-guided attenuation maps were generated using the different segmentation methods. Thereafter, quantitative analysis of PET attenuation correction was performed using CT-based attenuation correction as reference.

RESULTS

The SBA and MV methods resulted in considerable underestimation of bone identification (Dice ≈ 0.62) and high factious fragmentation error of contiguous structures. Applying global atlas selection or regularization (G-STAPLE and G-SIMPLE) to the SBA method enhanced bone segmentation accuracy up to a Dice = 0.66. The best results were achieved when applying the L-STAPLE method with a Dice of 0.76 and the L-Shp method with a Dice of 0.75. However, L-STAPLE required up to five-fold increased computation time compared to the L-Shp method. Moreover, both L-STAPLE and L-Shp methods resulted in less than 3% SUV mean relative error and 6% SUV mean absolute error in bony structures owing to superior bone identification accuracy. The quantitative analysis using joint histograms revealed good correlation between PET-MRAC images using the proposed L-Shp algorithm and the corresponding reference PET-CT images.

CONCLUSIONS

The performance of SBA was enhanced through application of local atlas weighting or regularization schemes (L-STAPLE and L-Shp). Bone recognition, fragmentation of the contiguous structures, and quantitative PET uptake recovery improved dramatically using these methods while the proposed L-Shp method significantly reduced the computation time.

Fast automated segmentation of multiple objects via spatially weighted shape learning

Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice's similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.

Imaging of femoroacetabular impingement-current concepts

Following the recognition of femoroacetabular impingement (FAI) as a clinical entity, diagnostic tools have continuously evolved. While the diagnosis of FAI is primarily made based on the patients' history and clinical examination, imaging of FAI is indispensable. Routine diagnostic work-up consists of a set of plain radiographs, magnetic resonance imaging (MRI) and MR-arthrography. Recent advances in MRI technology include biochemically sensitive sequences bearing the potential to detect degenerative changes of the hip joint at an early stage prior to their appearance on conventional imaging modalities. Computed tomography may serve as an adjunct. Advantages of CT include superior bone to soft tissue contrast, making CT applicable for image-guiding software tools that allow evaluation of the underlying dynamic mechanisms causing FAI. This article provides a summary of current concepts of imaging in FAI and a review of the literature on recent advances, and their application to clinical practice.

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

PURPOSE

The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT generation approach.

METHODS

The proposed approach consists of only one online registration between the target and reference images, regardless of the number of atlas images (N), while for the remaining atlas images, the pre-computed transformation matrices to the reference image are used to align them to the target image. The performance characteristics of the proposed method were evaluated and compared with conventional atlas-based attenuation map generation strategies (direct registration of the entire atlas images followed by voxel-wise weighting (VWW) and arithmetic averaging atlas fusion). To this end, four different positron emission tomography (PET) attenuation maps were generated via arithmetic averaging and VWW scheme using both direct registration and ORMA approaches as well as the 3-class attenuation map obtained from the Philips Ingenuity TF PET/MRI scanner commonly used in the clinical setting. The evaluation was performed based on the accuracy of extracted whole-body bones by the different attenuation maps and by quantitative analysis of resulting PET images compared to CT-based attenuation-corrected PET images serving as reference.

RESULTS

The comparison of validation metrics regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application of the ORMA approach modestly compromised the accuracy, yielding a Dice similarity measure of 0.76 ± 0.05 for ORMA-VWW and 0.55 ± 0.03 for ORMA-averaging. The results of quantitative PET analysis followed the same trend with less significant differences in terms of SUV bias, whereas massive improvements were observed compared to PET images corrected for attenuation using the 3-class attenuation map. The maximum absolute bias achieved by VWW and VWW-ORMA methods was 06.4 ± 5.5 in the lung and 07.9 ± 4.8 in the bone, respectively.

CONCLUSIONS

The proposed algorithm is capable of generating decent attenuation maps. The quantitative analysis revealed a good correlation between PET images corrected for attenuation using the proposed pseudo-CT generation approach and the corresponding CT images. The computational time is reduced by a factor of 1/N at the expense of a modest decrease in quantitative accuracy, thus allowing us to achieve a reasonable compromise between computing time and quantitative performance.

Bone marrow segmentation based on a combined consideration of transverse relaxation processes and Dixon oscillations

The aim of this study was to demonstrate that gradient-echo sampling of single spin echoes can be used to isolate the signal from trabecular bone marrow, with high-quality segmentation and surface reconstructions resulting from the application of simple post-processing strategies. Theoretical expressions of the time-domain single-spin-echo signal were used to simulate signals from bone marrow, non-bone fatty deposits and muscle. These simulations were compared with and used to interpret signals obtained by the application of the gradient-echo sampling of a spin-echo sequence to image the knee and surrounding tissues at 1.5 T. Trabecular bone marrow has a much higher reversible transverse relaxation rate than surrounding non-bone fatty deposits and other musculoskeletal tissues. This observation, combined with a choice of gradient-echo spacing that accentuates Dixon-type oscillations from chemical-shift interference effects, enabled the isolation of bone marrow signal from surrounding tissues through the use of simple image subtraction and thresholding. Three-dimensional renderings of the marrow surface were then readily generated with this approach - renderings that may prove useful for bone morphology assessment, e.g. for the measurement of femoral anteversion. In conclusion, understanding the behavior of signals from bone marrow and surrounding tissue as a function of time through a spin echo facilitates the segmentation and reconstruction of bone marrow surfaces using straightforward post-processing strategies that are typically available on modern radiology workstations.

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

Comparison of T2 Values in the Lateral and Medial Portions of the Weight-Bearing Cartilage of the Hip for Patients With Symptomatic Femoroacetabular Impingement and Asymptomatic Volunteers

PURPOSE

To develop a simplified method to define a clinically relevant subregion in the course of arthroscopic treatment of femoroacetabular impingement (FAI) using T2 mapping in patients and asymptomatic volunteers. Additionally, we sought to compare the lateral and medial subregion values in asymptomatic volunteers and in patients presenting with FAI. Finally, we wanted to investigate possible associations between patients' T2 mapping values and demographic variables-i.e., alpha angle, age, sex, and body mass index (BMI).

METHODS

Twenty-five asymptomatic volunteers and 23 consecutive symptomatic patients with FAI (cam or mixed type) were prospectively enrolled and evaluated with a sagittal T2 mapping sequence. The weight-bearing region of the acetabular and femoral cartilage was manually segmented and divided into medial and lateral subregions. Median T2 values were determined, and patient characteristics were assessed as potential predictors of T2 values.

RESULTS

T2 values in the lateral portion of the acetabulum were lower than in the medial portion for both asymptomatic volunteers (43 v 53 ms; P < .001) and patients with FAI (42 v 49 ms; P = .016). The medial acetabulum (MA) of asymptomatic volunteers had higher T2 values than those of the FAI group (53 v 49 ms; P = .040). The lateral-minus-medial difference was significantly larger among asymptomatic volunteers than in patients with FAI (P = .047). Patients with FAI had higher alpha angles than those of the asymptomatic volunteers, but no other associations with patient characteristics were observed.

CONCLUSIONS

This study's findings suggest that there are differences in cartilage T2 mapping values between medial and lateral weight-bearing aspects of the hip and may expand the application and usefulness of biochemical magnetic resonance imaging (MRI) techniques, specifically T2 mapping, in the diagnosis of hip cartilage damage with the evaluation of clinically relevant subregions. When comparing asymptomatic volunteers and patients with FAI presenting with cam or mixed type deformity, we observed a significant contrast between the T2 mapping values of the lateral and medial portions of the weight-bearing zone of the acetabular cartilage, whereas such contrast was not observed when zone 3 was analyzed as a whole.

LEVEL OF EVIDENCE

Level III, development of diagnostic criteria on the basis of consecutive patients with a universally applied reference gold standard.

Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation

Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a 'normal' shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R=0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07±1.00mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation.

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926  ±  0.050 and 0.837  ±  0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806  ±  0.133 for the humerus and 0.795  ±  0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.