Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis

Bone Position and Ligament Deformations of the Foot From CT Images to Quantify the Influence of Footwear in ex vivo Feet

The mechanical behavior of the foot is often studied through the movement of the segments composing it and not through the movement of each individual bone, preventing an accurate and unambiguous study of soft tissue strains and foot posture. In order to describe the internal behavior of the foot under static load, we present here an original methodology that automatically tracks bone positions and ligament deformations through a series of CT acquisitions for a foot under load. This methodology was evaluated in a limited clinical study based on three cadaveric feet in different static load cases, first performed with bare feet and then with a sports shoe to get first insights on how the shoe influences the foot's behavior in different configurations. A model-based tracking technique using hierarchical distance minimization was implemented to track the position of 28 foot bones for each subject, while a mesh-morphing technique mapped the ligaments from a generic model to the patient-specific model in order to obtain their deformations. Comparison of these measurements between the ex vivo loaded bare foot and the shod foot showed evidence that wearing a shoe affects the deformation of specific ligaments, has a significant impact on the relative movement of the bones and alters the posture of the foot skeleton (plantar-dorsal flexion, arch sagging, and forefoot abduction-adduction on the midfoot). The developed method may provide new clinical indicators to guide shoe design and valuable data for detailed foot model validation.

Objective Indirect Assessment of Transverse Ligament Competence Using Quantitative Analysis of 3-Dimensional Segmented Flexion-Extension Computed Tomography Scan

OBJECTIVE

Assessment of transverse ligament (TL) competence in patients with suspected atlantoaxial instability is performed via indirect radiograph measurements or direct TL visualization on magnetic resonance imaging (MRI). Interpretation of these images can be limited by unique patient anatomy or imaging technique variability. We report a novel technique for evaluating TL competence using flexion-extension computed tomography (feCT) scan with 3-dimensional (3D) segmentation and quantitative analysis.

METHODS

feCT scans of 11 patients were segmented to create 3D surface models. Six patients with atlantoaxial pathology were evaluated for possible instability based on clinical examination and imaging findings. The other 5 patients had no clinical or imaging evidence of atlantoaxial injury. Dynamic atlantodental interval (ADI) was calculated using point-to-point voxel changes between flexion and extension 3D models. Magnitude and direction of ADI changes were quantified and compared with available cervical spine flexion-extension radiograph and/or MRI findings.

RESULTS

In the 5 patients without evidence of atlantoaxial injury, 94.3% of ADI vector changes were <3.0 mm. In the 3 patients with atlantoaxial pathology but TL competence, 92.4% of ADI vector changes were <3.0 mm. In the 3 patients with atlantoaxial pathology and TL incompetence, only 49.1% of ADI vector changes were <3.0 mm. In addition to the significant atlantoaxial subluxation in these 3 patients, there was significant rotational motion compared with the patients with an intact TL.

CONCLUSIONS

3D segmentation and quantitative analysis of feCT scan allow objective indirect assessment of TL integrity. Results are consistent with MRI findings and offer additional biomechanical information regarding the direction and distribution of atlantoaxial motion.

3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review

This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.

Statistical shape models of cuboid, navicular and talus bones

BACKGROUND

The aim was to develop statistical shape models of the main human tarsal bones that would result in novel representations of cuboid, navicular and talus.

METHODS

Fifteen right and 15 left retrospectively collected computed tomography data sets from male individuals, aged from 17 to 63 years, with no known foot pathology were collected. Data were gathered from 30 different subjects. A process of model building includes image segmentation, unifying feature position, mathematical shape description and obtaining statistical shape geometry.

RESULTS

Orthogonal decomposition of bone shapes utilising spherical harmonics was employed providing means for unique parametric representation of each bone. Cross-validated classification results based on parametric spherical harmonics representation showed high sensitivity and high specificity greater than 0.98 for all considered bones.

CONCLUSIONS

The statistical shape models of cuboid, navicular and talus created in this work correspond to anatomically accurate atlases that have not been previously considered. The study indicates high clinical potential of statistical shape modelling in the characterisation of tarsal bones. Those novel models can be applied in medical image analysis, orthopaedics and biomechanics in order to provide support for preoperative planning, better diagnosis or implant design.

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.

Three-dimensional kinematic stress magnetic resonance image analysis shows promise for detecting altered anatomical relationships of tissues in the cervical spine associated with painful radiculopathy

For some patients with radiculopathy a source of nerve root compression cannot be identified despite positive electromyography (EMG) evidence. This discrepancy hampers the effective clinical management for these individuals. Although it has been well-established that tissues in the cervical spine move in a three-dimensional (3D) manner, the 3D motions of the neural elements and their relationship to the bones surrounding them are largely unknown even for asymptomatic normal subjects. We hypothesize that abnormal mechanical loading of cervical nerve roots during pain-provoking head positioning may be responsible for radicular pain in those cases in which there is no evidence of nerve root compression on conventional cervical magnetic resonance imaging (MRI) with the neck in the neutral position. This biomechanical imaging proof-of-concept study focused on quantitatively defining the architectural relationships between the neural and bony structures in the cervical spine using measurements derived from 3D MR images acquired in neutral and pain-provoking neck positions for subjects: (1) with radicular symptoms and evidence of root compression by conventional MRI and positive EMG, (2) with radicular symptoms and no evidence of root compression by MRI but positive EMG, and (3) asymptomatic age-matched controls. Function and pain scores were measured, along with neck range of motion, for all subjects. MR imaging was performed in both a neutral position and a pain-provoking position. Anatomical architectural data derived from analysis of the 3D MR images were compared between symptomatic and asymptomatic groups, and the symptomatic groups with and without imaging evidence of root compression. Several differences in the architectural relationships between the bone and neural tissues were identified between the asymptomatic and symptomatic groups. In addition, changes in architectural relationships were also detected between the symptomatic groups with and without imaging evidence of nerve root compression. As demonstrated in the data and a case study the 3D stress MR imaging approach provides utility to identify biomechanical relationships between hard and soft tissues that are otherwise undetected by standard clinical imaging methods. This technique offers a promising approach to detect the source of radiculopathy to inform clinical management for this pathology.

Mean-shifted surface curvature algorithm for automatic bone shape segmentation in orthopedic surgery planning: a sensitivity analysis

The results of recent studies concerning statistical bone atlases and automated shape analysis are promising with a view to widening the use of surface models in orthopedic clinical practice, both in pre-operative planning and in the intra-operative stages. In this domain, automatic shape analysis is strongly advocated because it offers the opportunity to detect morphological and clinical landmarks with superior repeatability in comparison to human operators. Surface curvatures have been proposed extensively for segmentation and labeling of image and surface regions based on their appearance and shape. The surface curvature is an invariant that can be exploited for reliable detection of geometric features. In this paper, we investigate the potentiality of the algorithm termed mean-shift (MS), as applied to a non-linear combination of the minimum and maximum curvatures of a surface. We exploited a sensitivity analysis of the algorithm parameters across increasing surface resolutions. Results obtained with femur and pelvic bone surface data, reconstructed from cadaveric CT scans, demonstrated that the information content derived by the MS non-linear curvature overcomes both the mean and the Gaussian curvatures and the original non-linear curvature. By applying a threshold-based clustering algorithm to the curvature distribution, we found that the number of clusters yielded by the MS non-linear curvature is significantly lower (by a factor of up to 6) than that obtained by using the original non-linear curvature. In conclusion, this study provides valuable insights into the use of surface curvature for automatic shape analysis.

EM segmentation of the distal femur and proximal tibia: a high-throughput approach to anatomic surface generation

Fully automated segmentation of computed tomography (CT) images remains a challenge for musculoskeletal researchers. The surfaces generated from image segmentations are valuable for surgical evaluation and planning. Previously, we demonstrated the expectation maximization (EM) algorithm as a semi-automated method of bone segmentation from CT images. In this work, we improve upon the methodology of probability map generation and demonstrate extended applicability of EM-based segmentation to the distal femur and proximal tibia using 72 CT image sets. We also compare the resulting EM segmentations to manual tracings using overlap metrics and time. In the case of the distal femur, the resulting quality metrics had mean values of 0.91 and 0.95 for the Jaccard and Dice metrics, respectively. For the proximal tibia, the Jaccard and Dice metrics were 0.90 and 0.95, respectively. The EM segmentation method was 8 times faster than the average manual segmentation and required less than 4% of the human rater time. Overall, the EM algorithm offers reliable image segmentations with an increased efficiency in comparison to manual segmentation techniques.

Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis

There are several medical application areas that require the segmentation and separation of the component bones of joints in a sequence of images of the joint acquired under various loading conditions, our own target area being joint motion analysis. This is a challenging problem due to the proximity of bones at the joint, partial volume effects, and other imaging modality-specific factors that confound boundary contrast. In this article, a two-step model-based segmentation strategy is proposed that utilizes the unique context of the current application wherein the shape of each individual bone is preserved in all scans of a particular joint while the spatial arrangement of the bones alters significantly among bones and scans. In the first step, a rigid deterministic model of the bone is generated from a segmentation of the bone in the image corresponding to one position of the joint by using the live wire method. Subsequently, in other images of the same joint, this model is used to search for the same bone by minimizing an energy function that utilizes both boundary- and region-based information. An evaluation of the method by utilizing a total of 60 data sets on MR and CT images of the ankle complex and cervical spine indicates that the segmentations agree very closely with the live wire segmentations, yielding true positive and false positive volume fractions in the range 89%-97% and 0.2%-0.7%. The method requires 1-2 minutes of operator time and 6-7 min of computer time per data set, which makes it significantly more efficient than live wire-the method currently available for the task that can be used routinely.