FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications

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

Fully Automatic Segmentation of Hip CT Images

Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively

Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images

PURPOSE: Accurate preoperative planning is crucial for the outcome of total hip arthroplasty. Recently, 2D pelvic X-ray radiographs have been replaced by 3D CT. However, CT suffers from relatively high radiation dosage and cost. An alternative is to reconstruct a 3D patient-specific volume data from 2D X-ray images. METHODS: In this paper, based on a fully automatic image segmentation algorithm, we propose a new control point-based 2D-3D registration approach for a deformable registration of a 3D volumetric template to a limited number of 2D calibrated X-ray images and show its application to personalized reconstruction of 3D volumes of the proximal femur. The 2D-3D registration is done with a hierarchical two-stage strategy: the scaled-rigid 2D-3D registration stage followed by a regularized deformable B-spline 2D-3D registration stage. In both stages, a set of control points with uniform spacing are placed over the domain of the 3D volumetric template first. The registration is then driven by computing updated positions of these control points with intensity-based 2D-2D image registrations of the input X-ray images with the associated digitally reconstructed radiographs, which allows computing the associated registration transformation at each stage. RESULTS: Evaluated on datasets of 44 patients, our method achieved an overall surface reconstruction accuracy of [Formula: see text] and an average Dice coefficient of [Formula: see text]. We further investigated the cortical bone region reconstruction accuracy, which is important for planning cementless total hip arthroplasty. An average cortical bone region Dice coefficient of [Formula: see text] and an inner cortical bone surface reconstruction accuracy of [Formula: see text] were found. CONCLUSIONS: In summary, we developed a new approach for reconstruction of 3D personalized volumes of the proximal femur from 2D X-ray images. Comprehensive experiments demonstrated the efficacy of the present approach

Localization and Segmentation of 3D Intervertebral Discs from MR Images via a Learning Based Method: A Validation Framework

In this paper, we present the results of evaluating our fully automatic intervertebral disc (IVD) localization and segmentation method using the training data and the test data provided by the localization and segmentation challenge organizers of the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015. We introduce a validation framework consisting of four standard evaluation criteria to evaluate the performance of our method for both localization and segmentation tasks. More specifically, for localization we propose to use the mean localization distance (MLD) with standard deviation (SD) as well as the successful detection rate with three ranges of accuracy. For segmentation, we propose to use the Dice overlap coefficients (DOC) and average absolute distance (AAD) between the automatic segmented disc surfaces and the associated ground truth. Using the proposed metrics, we first validate our previously introduced approach by conducting a comprehensive leave-one-out experiment on the IVD challenge training data which consists of 15 three-dimensional T2-weighted turbo spin echo magnetic resonance (MR) images and the associated ground truth. For localization, we respectively achieved a successful detection rate of 61, 92, and 93%93% when the accuracy range is set to 2.0, 4.0, and 6.0 mm, and a mean localization error of 1.8±0.91.8±0.9 mm. For segmentation, we obtained a mean DOC of 88%88% and a mean AAD of 1.4 mm. We further evaluated the performance of our approach on the test-1 dataset consisting of five MR images released at the pre-test stage and the test-2 dataset consisting of another five MR images released at the on-site competition stage. The results were obtained with a blind test where the performance evaluations were conducted by the challenge organizers. For localization on the test-1 dataset we achieved a successful detection rate of 91.4, 100.0, and 100.0%100.0% with a MLD ±± SD of 1.0±0.81.0±0.8 mm, and for localization on the test-2 dataset we achieved a successful detection rate of 77.1, 100.0, and 100.0%100.0% with a MLD ±± SD of 1.4±0.71.4±0.7 mm, respectively. For segmentation on the test-1 dataset we obtained a mean DOC of 90%90% and a mean AAD of 1.2 mm, and for segmentation on the test-2 dataset we obtained a mean DOC of 92%92% and a mean AAD of 1.3 mm, respectively

Localization and Segmentation of 3D Intervertebral Discs from MR Images via a Learning Based Method: A Validation Framework

In this paper, we present the results of evaluating our fully automatic intervertebral disc (IVD) localization and segmentation method using the training data and the test data provided by the localization and segmentation challenge organizers of the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015. We introduce a validation framework consisting of four standard evaluation criteria to evaluate the performance of our method for both localization and segmentation tasks. More specifically, for localization we propose to use the mean localization distance (MLD) with standard deviation (SD) as well as the successful detection rate with three ranges of accuracy. For segmentation, we propose to use the Dice overlap coefficients (DOC) and average absolute distance (AAD) between the automatic segmented disc surfaces and the associated ground truth. Using the proposed metrics, we first validate our previously introduced approach by conducting a comprehensive leave-one-out experiment on the IVD challenge training data which consists of 15 three-dimensional T2-weighted turbo spin echo magnetic resonance (MR) images and the associated ground truth. For localization, we respectively achieved a successful detection rate of 61, 92, and 93%93% when the accuracy range is set to 2.0, 4.0, and 6.0 mm, and a mean localization error of 1.8±0.91.8±0.9 mm. For segmentation, we obtained a mean DOC of 88%88% and a mean AAD of 1.4 mm. We further evaluated the performance of our approach on the test-1 dataset consisting of five MR images released at the pre-test stage and the test-2 dataset consisting of another five MR images released at the on-site competition stage. The results were obtained with a blind test where the performance evaluations were conducted by the challenge organizers. For localization on the test-1 dataset we achieved a successful detection rate of 91.4, 100.0, and 100.0%100.0% with a MLD ±± SD of 1.0±0.81.0±0.8 mm, and for localization on the test-2 dataset we achieved a successful detection rate of 77.1, 100.0, and 100.0%100.0% with a MLD ±± SD of 1.4±0.71.4±0.7 mm, respectively. For segmentation on the test-1 dataset we obtained a mean DOC of 90%90% and a mean AAD of 1.2 mm, and for segmentation on the test-2 dataset we obtained a mean DOC of 92%92% and a mean AAD of 1.3 mm, respectively

FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0±1.5°, 2.1±1.6°, and 3.5±2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications

Fully Automatic Segmentation of Hip CT Images via Random Forest Regression-Based Atlas Selection and Optimal Graph Search-Based Surface Detection

Automatic extraction of surface models of both pelvis and proximal femur of a hip joint from 3D CT images is an important and challenging task for computer assisted diagnosis and planning of periacetabular osteotomy (PAO). Due to the narrowness of hip joint space, the adjacent surfaces of the acetabulum and the femoral head are hardly distinguishable from each other in the target CT images. This paper presents a fully automatic method for segmenting hip CT images using random forest (RF) regression-based atlas selection and optimal graph search-based surface detection. The two fundamental contributions of our method are: (1) An efficient RF regression framework is developed for a fast and accurate landmark detection from the hip CT images. The detected landmarks allow for not only a robust and accurate initialization of the atlases within the target image space but also an effective selection of a subset of atlases for a fast atlas-based segmentation; and (2) 3-D graph theory-based optimal surface detection is used to refine the extraction of the surfaces of the acetabulum and the femoral head with the ultimate goal to preserve hip joint structure and to avoid penetration between the two extracted surfaces. Validation on 30 hip CT images shows that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur with an average accuracy of 0.56 mm, 0.61 mm, and 0.57 mm, respectively

Statistical shape modeling of compound musculoskeletal structures around the thigh region

Accurate 3D models of lower extremity are required for model-based simulations in kinematic analysis of musculoskeletal (MS) system. In this paper, we present a modeling framework which combines a hybrid registration scheme with an articulated statistical shape model (aSSM) construction technique. The present modeling framework is used to develop an aSSM of compound MS structures based on a training set of 12 single side CT images with the associated ground-truth segmentation of 7 structures around the thigh region. By incorporating 90% of the training set variations, the model exhibits a generalization ability of 2.77±0.48 mm and specificity of 2.87±0.43 mm. The constructed aSSM has potential applications in model-based 2D-3D construction, 3D medical image segmentation, and kinematic analysis of MS system. To the best of our knowledge, this is the first 3D aSSM of compound MS structures around the thigh region

Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively