Segmentation Of Proximal Femur Research Articles

FULLY AUTOMATIC SYSTEM TO DETECT AND SEGMENT THE PROXIMAL FEMUR IN PELVIC RADIOLOGICAL IMAGES FOR LEGG-CALVE-PERTHES DISEASE

The most important outcome predictor of Legg-Calvé-Perthes disease (LCPD) is the shape of the healed femoral head. However, the deformity of the femoral head is currently evaluated by non-reproducible, categorical, and qualitative classifications. In this regard, recent advances in computer vision might provide the opportunity to automatically detect and delineate the outlines of bone in radiographic images for calculating a continuous measure of femoral head deformity. This study aimed to construct a pipeline for accurately detecting and delineating the proximal femur in radiographs of LCPD patients employing existing algorithms. To detect the proximal femur, the pretrained stateof-the-art object detection model, YOLOv5, was trained on 1580 manually annotated radiographs, validated on 338 radiographs, and tested on 338 radiographs. Additionally, 200 radiographs of shoulders and chests were added to the dataset to make the model more robust to false positives and increase generalizability. The convolutional neural network architecture, U-Net, was then employed to segment the detected proximal femur. The network was trained on 80 manually annotated radiographs using real-time data augmentation to increase the number of training images and enhance the generalizability of the segmentation model. The network was validated on 60 radiographs and tested on 60 radiographs. The object detection model achieved a mean Average Precision (mAP) of 0.998 using an Intersection over Union (IoU) threshold of 0.5, and a mAP of 0.712 over IoU thresholds of 0.5 to 0.95 on the test set. The segmentation model achieved an accuracy score of 0.912, a Dice Coefficient of 0.937, and a binary IoU score of 0.854 on the test set. The proposed fully automatic proximal femur detection and segmentation system provides a promising method for accurately detecting and delineating the proximal femoral bone contour in radiographic images, which is necessary for further image analysis.

Fully automatic system to detect and segment the proximal femur in pelvic radiographic images for Legg-Calvé-Perthes disease.

This study aimed to develop a method using computer vision techniques to accurately detect and delineate the proximal femur in radiographs of Legg-Calvé-Perthes disease (LCPD) patients. Currently, evaluating femoral head deformity, a crucial predictor of LCPD outcomes, relies on unreliable categorical and qualitative classifications. To address this limitation, we employed the pretrained object detection model YOLOv5 to detect the proximal femur on over 2000 radiographs, including images of shoulders and chests, to enhance robustness and generalizability. Subsequently, we utilized the U-Net convolutional neural network architecture for image segmentation of the proximal femur in more than 800 manually annotated images of stage IV LCPD. The results demonstrate outstanding performance, with the object detection model achieving high accuracy (mean average precision of 0.99) and the segmentation model attaining an accuracy score of 91%, dice coefficient of 0.75, and binary IoU score of 0.85 on the held-out test set. The proposed fully automatic proximal femur detection and segmentation system offers a promising approach to accurately detect and delineate the proximal femoral bone contour in radiographic images, which is essential for further image analysis in LCPD patients.Clinical significance: This study highlights the potential of computer vision techniques for enhancing the reliability ofLegg-Calvé-Perthes diseasestaging and outcome prediction.

Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images.

BackgroundFemoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully automated 3D pipeline for segmentation, statistical shape assessment and measurement of cam volume, surface area and height from clinical magnetic resonance (MR) images of the hip in FAI patients.MethodsThe novel CamMorph pipeline involves two components: (I) accurate proximal femur segmentation generated by combining the 3D U-net to identify both global (region) and local (edge) features in clinical MR images and focused shape modelling to generate a 3D anatomical model for creating patient-specific proximal femur models; (II) patient-specific anatomical information from 3D focused shape modelling to simulate ‘healthy’ femoral bone models with cam-affected region constraints applied to the anterosuperior femoral head-neck region to quantify cam morphology in FAI patients. The CamMorph pipeline, which generates patient-specific data within 5 min, was used to analyse multi-site clinical MR images of the hip to measure and assess cam morphology in male (n=56) and female (n=41) FAI patients.ResultsThere was excellent agreement between manual and CamMorph segmentations of the proximal femur as demonstrated by the mean Dice similarity index (DSI; 0.964±0.006), 95% Hausdorff distance (HD; 2.123±0.876 mm) and average surface distance (ASD; 0.539±0.189 mm) values. Compared to female FAI patients, male patients had a significantly larger median cam volume (969.22 vs. 272.97 mm3, U=240.0, P<0.001), mean surface area [657.36 vs. 306.93 mm2, t(95)=8.79, P<0.001], median maximum-height (3.66 vs. 2.15 mm, U=407.0, P<0.001) and median average-height (1.70 vs. 0.86 mm, U=380.0, P<0.001).ConclusionsThe fully automated 3D CamMorph pipeline developed in the present study successfully segmented and measured cam morphology from clinical MR images of the hip in male and female patients with differing FAI severity and pathoanatomical characteristics.

A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.

Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip fracture risk. In this study, we proposed an approach based on deep learning for the fast and automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments. A three-dimensional (3D) end-to-end fully convolutional neural network (CNN), which can better combine the information among neighbor slices and get more accurate segmentation results by 3D CNN, was developed for our segmentation task. The separation of cortical and trabecular bones derived from the QCT software MIAF-Femur was used as the segmentation reference. Two models with the same network structures were trained, and they achieved a dice similarity coefficient (DSC) of 97.82% and 96.53% for the periosteal and endosteal contours, respectively. Compared with MIAF-Femur, it takes half an hour to segment a case, and our CNN model takes a few minutes. To verify the excellent performance of our model for proximal femoral segmentation, we measured the volumes of different parts of the proximal femur and compared it with the ground truth, and the relative errors of femur volume between predicted result and ground truth are all less than 5%. This approach will be expected helpful to measure the bone mineral densities of cortical and trabecular bones, and to evaluate the bone strength based on FEA.

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.

Bone and joint enhancement filtering: Application to proximal femur segmentation from uncalibrated computed tomography datasets.

Methods for reliable femur segmentation enable the execution of quality retrospective studies and building of robust screening tools for bone and joint disease. An enhance-and-segment pipeline is proposed for proximal femur segmentation from computed tomography datasets. The filter is based on a scale-space model of cortical bone with properties including edge localization, invariance to density calibration, rotation invariance, and stability to noise. The filter is integrated with a graph cut segmentation technique guided through user provided sparse labels for rapid segmentation. Analysis is performed on 20 independent femurs. Rater proximal femur segmentation agreement was 0.21mm (average surface distance), 0.98 (Dice similarity coefficient), and 2.34mm (Hausdorff distance). Manual segmentation added considerable variability to measured failure load and volume (CVRMS>5%) but not density. The proposed algorithm considerably improved inter-rater reproducibility for all three outcomes (CVRMS<0.5%). The algorithm localized the periosteal surface accurately compared to manual segmentation but with a slight bias towards a smaller volume. Hessian-based filtering and graph cut segmentation localizes the periosteal surface of the proximal femur with comparable accuracy and improved precision compared to manual segmentation.

Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.

Deep Learning-Based Automatic Segmentation of the Proximal Femur from MR Images.

This chapter addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Inspired by previous work, multi-level deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on 20 3D MR images of femoroacetabular impingement patients. The experimental results demonstrate the efficacy of the present method.

Graph-Cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior

Femur segmentation from computed tomography (CT) images is a fundamental problem in femur-related computer-assisted diagnosis and surgical planning/navigation. In this study, an automatic approach for the segmentation of proximal femur from CT images that incorporates the statistical shape prior into the graph-cut framework (SP-GC) is proposed. The proposed segmentation framework includes two major processes, namely training and segmentation. In the training stage, a training set of three-dimensional CT images from a group of normal subjects were segmented semi-automatically. The shape prior was generated by active shape modeling that included mean shape and shape variance information. In the segmentation stage, two shape terms originated from the training stage were included in the GC energy function. The minimization of the energy function was achieved using a max-flow/min-cut algorithm. The performance of the proposed segmentation method was evaluated by testing on 60 CT datasets from bilateral femurs of 30 normal subjects. Qualitative and quantitative analyses of the segmentation results of the proposed method were performed and the results were compared with two widely used methods, namely the active shape model (ASM) and traditional GC, with results from manual delineation used as the ground truth. The mean dice similarity coefficient of the proposed SP-GC was 0.9600, which is higher than those of ASM and GC (0.8769 and 0.9358, respectively). The mean normalized error rate of the SP-GC results was 10 and 6 % lower than those of ASM and GC, respectively. In terms of the average surface distance measurement, the value for SP-GC was 0.885 mm, compared with 2.148 and 1.154 mm for ASM and GC, respectively. In comparison with ASM, SP-GC has superior performance given a small training set (e.g., n = 12). With increasing number of training samples, the segmentation accuracy of ASM saturated, but that of SP-GC slightly increased.

A 3D active model framework for segmentation of proximal femur in MR images

Segmentation of osseous structures from clinical MR images is difficult due to acquisition artifacts and variable signal intensity of bones. Segmentation of femoral head is required for evaluation of hip joint abnormalities such as cam- type femoroacetabular impingement. A parametric deformable model (PDM) framework was developed for segmentation of 3D magnetic resonance (MR) images of the hip. A two-phase segmentation scheme was implemented: (i) Radial basis function interpolation was performed for semi-automatic piecewise registration of a proximal femur atlas model to an MRI scan region of interest. User-defined control points on the mesh model were registered to the corresponding landmarks on the image. (ii) An active PDM was then used for coarse-to-fine level segmentation. The segmentation technique was tested using 3D synthetic image data and clinical MR scans of the hip with varying resolution. The segmentation method provided a mean target overlap of 0.95 and misclassification error of 0.035 for the synthetic data. The average target overlap was 0.88, and misclassification error rate was 0.12 for the clinical MRI data sets. A framework for segmentation of proximal femur in hip MRI scans was developed and tested. This method is robust to artifacts and intensity inhomogeneity and resistant to leakage into adjacent tissues. In comparison with slicewise segmentation techniques, this method features inter-slice consistency, which results in a smooth model of the proximal femur in hip MRI scans.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs

Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was 0.96 mm ± 0.35 mm, requiring <5 s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Proximal femur segmentation in conventional pelvic x ray

A solid and accurate proximal femur segmentation technique using the popular active shape model (ASM) is proposed. For generating an optimal shape prior, the minimum description length, based on 200 supervised manual segmented proximal femur shapes, is used. The segmentation is based on a coarse to fine scaling technique including a profile scale space method. The segmentation results are compared using an optimal defined initial pose and a pose based on a registration technique. Using ideal template initialization, 95% of the shapes have been recovered exactly (average point-to-point error approximately 13 pixels, average point-to-boundary error approximately 7 pixels). Using a template-based initialization based on a registration technique, a successful segmentation rate of approximately 89% is achieved, with an average point-to-point error approximately 12 pixels, and an average point-to-boundary error approximately 8 pixels. With an adequate template initialization and an improved ASM, this method seems to provide an accurate tool for segmentation of the proximal femur shapes on conventional hip overview x-ray images.