Futoshi Yokota

Automated Segmentation of the Femur and Pelvis from 3D CT Data of Diseased Hip Using Hierarchical Statistical Shape Model of Joint Structure

Segmentation of the femur and pelvis from 3D data is prerequisite of patient specific planning and simulation for hip surgery. Separation of the femoral head and acetabulum is one of main difficulties in the diseased hip joint due to deformed shapes and extreme narrowness of the joint space. In this paper, we develop a hierarchical multi-object statistical shape model representing joint structure for automated segmentation of the diseased hip from 3D CT images. In order to represent shape variations as well as pose variations of the femur against the pelvis, both shape and pose variations are embedded in a combined pelvis and femur statistical shape model (SSM). Further, the whole combined SSM is divided into individual pelvis and femur SSMs and a partial combined SSM only including the acetabulum and proximal femur. The partial combined SSM maintains the consistency of the two bones by imposing the constraint that the shapes of the overlapped portions of the individual and partial combined SSMs are identical. The experimental results show that segmentation and separation accuracy of the femur and pelvis was improved using the proposed method compared with independent use of the pelvis and femur SSMs.

Automated CT Segmentation of Diseased Hip Using Hierarchical and Conditional Statistical Shape Models

Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge in diseased hip joints because of deformed shapes and extreme narrowness of the joint space. To overcome this difficulty, we investigated a multi-stage method in which the hierarchical hip statistical shape model (SSM) is initially utilized to complete segmentation of the pelvis and distal femur, and then the conditional femoral head SSM is used under the condition that the regions segmented during the previous stage are known. CT data from 100 diseased patients categorized on the basis of their disease type and severity, which included 200 hemi-hips, were used to validate the method, which delivered significantly increased segmentation accuracy for the femoral head.

Cup Implant Planning Based on 2-D/3-D Radiographic Pelvis Reconstruction—First Clinical Results

Goal: In the following, we will present a newly developed X-ray calibration phantom and its integration for 2-D/3-D pelvis reconstruction and subsequent automatic cup planning. Two different planning strategies were applied and evaluated with clinical data. Methods: Two different cup planning methods were investigated: The first planning strategy is based on a combined pelvis and cup statistical atlas. Thereby, the pelvis part of the combined atlas is matched to the reconstructed pelvis model, resulting in an optimized cup planning. The second planning strategy analyzes the morphology of the reconstructed pelvis model to determine the best fitting cup implant. Results: The first planning strategy was compared to 3-D CT-based planning. Digitally reconstructed radiographs of THA patients with differently severe pathologies were used to evaluate the accuracy of predicting the cup size and position. Within a discrepancy of one cup size, the size was correctly identified in 100% of the cases for Crowe type I datasets and in 77.8% of the cases for Crowe type II, III, and IV datasets. The second planning strategy was analyzed with respect to the eventually implanted cup size. In seven patients, the estimated cup diameter was correct within one cup size, while the estimation for the remaining five patients differed by two cup sizes. Conclusion: While both planning strategies showed the same prediction rate with a discrepancy of one cup size (87.5%), the prediction of the exact cup size was increased for the statistical atlas-based strategy (56%) in contrast to the anatomically driven approach (37.5%). Significance: The proposed approach demonstrated the clinical validity of using 2-D/3-D reconstruction technique for cup planning.

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.

Patient-Specific Skeletal Muscle Fiber Modeling from Structure Tensor Field of Clinical CT Images

We propose an optimization method for estimating patient-specific muscle fiber arrangement from clinical CT. Our approach first computes the structure tensor field to estimate local orientation, then a geometric template representing fiber arrangement is fitted using a B-spline deformation by maximizing fitness of the local orientation using a smoothness penalty. The initialization is computed with a previously proposed algorithm that takes account of only the muscle’s surface shape. Evaluation was performed using a CT volume (1.0 mm\(^\text {3}\)/voxel) and high resolution optical images of a serial cryo-section (0.1 mm\(^\text {3}\)/voxel). The mean fiber distance error at the initialization of 6.00 mm was decreased to 2.78 mm after the proposed optimization for the gluteus maximus muscle, and from 5.28 mm to 3.09 mm for the gluteus medius muscle. The result from 20 patient CT images suggested that the proposed algorithm reconstructed an anatomically more plausible fiber arrangement than the previous method.

Prediction of forearm bone shape based on partial least squares regression from partial shape

Computer‐assisted corrective osteotomy using a mirror image of the normal contralateral shape as reference is increasingly used. Instead, we propose to use the shape predicted by statistical learning to deal with cases demonstrating bilateral abnormality, such as bilateral trauma, congenital disease, and metabolic disease.

Shape-based acetabular cartilage segmentation: application to CT and MRI datasets.

PurposeA new method for acetabular cartilage segmentation in both computed tomography (CT) arthrography and magnetic resonance imaging (MRI) datasets with leg tension is developed and tested.MethodsThe new segmentation method is based on the combination of shape and intensity information. Shape information is acquired according to the predictable nonlinear relationship between the U-shaped acetabulum region and acetabular cartilage. Intensity information is obtained from the acetabular cartilage region automatically to complete the segmentation procedures. This method is evaluated using 54 CT arthrography datasets with two different radiation doses and 20 MRI datasets. Additionally, the performance of this method in identifying acetabular cartilage is compared with four other acetabular cartilage segmentation methods.ResultsThis method performed better than the comparison methods. Indeed, this method maintained good accuracy level for 74 datasets independent of the cartilage modality and with minimum user interaction in the bone segmentation procedures. In addition, this method was efficient in noisy conditions and in detection of the damaged cartilages with zero thickness, which confirmed its potential clinical usefulness.ConclusionsOur new method proposes acetabular cartilage segmentation in three different datasets based on the combination of the shape and intensity information. This method executes well in situations where there are clear boundaries between the acetabular and femoral cartilages. However, the acetabular cartilage and pelvic bone information should be obtained from one dataset such as CT arthrography or MRI datasets with leg traction.

Construction and Application of Large-Scale Image Database in Orthopedic Surgery

Databases of medical images are valuable resources not only for clinical studies such as the analysis of disease progression or a large-scale population analysis of morphological characteristics but also for those engaged in image analysis. Databases can serve as a compendium against which newly developed algorithms can be tested and a common platform for performance comparisons with existing state-of-the-art algorithms. Several database projects that have focused on certain target modalities and diseases have been successful, including the Cancer Imaging Archive, the Alzheimer’s Disease Neuroimaging Initiative, and the Osteoarthritis Initiative. Here, we introduce our efforts to construct a database of medical images and treatment records of Japanese patients who underwent hip surgery. This database currently contains computed tomography images, radiographs, and the log files of a surgical navigation system, including preoperative plans, intraoperative procedures, and postoperative outcomes (alignment). Herein, we also introduce our attempts in three applications: statistical analysis of the alignment in functional (standing) position, muscle function, and statistical analysis of surgeons’ expertise from the surgical log. Open access is an important aspect for the research community, but privacy is a concern, especially for large-scale databases where per-patient consent is difficult to obtain as well as with images of patients with specific diseases wherein complete de-identification is extremely difficult. We believe our effort serves as a step toward augmentation of social acceptability of the strength of medical image databases for accelerating advanced medical treatment.

Reconstruction of 3D Muscle Fiber Structure Using High Resolution Cryosectioned Volume

Three-dimensional (3D) muscle fiber architecture is important in patient-specific biomechanical simulation. While several in-vivo methods using diffusion tensor imaging and ultrasound have been demonstrated their feasibility in reconstruction of the fiber architecture, the main challenge is the lack of gold standard. Although physical measurement from cadavers has been considered as the accurate way of determining 3D muscle fiber architecture, its downsides include error in the manual tracing and the labor intensive process allowing only sparse sampling of a particular muscle. We propose an alternative method of obtaining a dense fiber architecture of multiple muscles in close proximity using high resolution cryosectioned images. Similar to the diffusion tensor imaging, we first extract the local orientation at each voxel using the structure tensor analysis and then tractography algorithm is applied to obtain stream lines. The proposed method was applied to all muscles around the hip joint and the masticatory muscles. Qualitative comparison with the anatomy textbook indicated that the proposed method reconstructed a plausible muscle fiber architecture. We plan to make the reconstructed fiber architecture of whole body muscles publicly available in order to serve for the biomechanics community.

Automated Segmentation and Anatomical Labeling of Abdominal Arteries Based on Multi-organ Segmentation from Contrast-Enhanced CT Data

A fully automated method is described for segmentation and anatomical labeling of the abdominal arteries from contrast-enhanced CT data of the upper abdomen. By assuming that the regions of the organs and aorta have already been automatically segmented, the problem is formulated as extracting and selecting the optimal paths between the organ and aorta regions based on a basic anatomical constraint that arteries supplying blood to an organ consist of tree structures whose root nodes are located in the aorta region and leaf nodes in the organ region. Using the constraint, the proposed method solves both of artery segmentation and anatomical labeling. In addition, the method is robust against topological variability of the branching patterns. Experimental results using 10 datasets demonstrate that the proposed method was effectively applied to several kinds of the abdominal arteries, which include the hepatic, splenic, and renal arteries. The average F-measure, which is a normalized accuracy measure taking both false positives and true negatives into account, was 0.89 for the proposed and 0.74 for the previous methods. The method could also effectively deal with topological variability of the hepatic and renal arteries.