Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis

Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of paired MR-CT data, which is rare, especially in the case of scoliotic patients. In this paper, we present a completely unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT volumes for easy segmentation of the vertebral bones. The resulting segmentation is used to reconstruct a 3D model of the spine. We validate our method on 28 scoliotic vertebrae in 3 patients by computing the point-to-surface mean distance between the landmark points for each vertebra obtained from pre-operative X-rays and the surface of the segmented vertebra. Our study results in a mean error of 3.41 $\pm$ 1.06 mm. Based on qualitative and quantitative results, we conclude that our method is able to obtain a good segmentation and 3D reconstruction of scoliotic spines, all after training from unpaired data in an unsupervised manner.Comment: To appear in the Proceedings of the SPIE Medical Imaging Conference 2021, San Diego, CA. 9 pages, 4 figures in tota

Automatic Generation and Novel Validation of Patient-Specific, Anatomically Inclusive Scoliosis Models for Biomechanics-Informed Surgical Planning

Scoliosis is an abnormal spinal curvature of greater than 10 degrees. Severe scoliotic deformities are addressed with highly invasive procedures: anterior or posterior spinal fusion approaches. This invasiveness is due, in part, to the constraints of current surgical planning, which utilizes computed tomography (CT) scans unable to discern spinal ligaments that are dissected to make the spine sufficiently compliant for correction. If localization of ligaments and soft tissues were achieved pre-operatively, corrective procedures could become safer and more efficient by using finite element (FE) biomechanical simulations to determine decreased incidences of ligament releases. This research aims to achieve ligament localization within CT scans by deforming computer-aided design (CAD) meshes that encompass vertebrae, intervertebral discs, ligaments, and other soft tissues to emulate patient-specific anatomy. Models are generated through deformable surface algorithms that elastically fit CAD meshes onto segmentations of conspicuous structures. Surrounding soft tissues are locally warped to reconstruct contextually appropriate positions before the CAD mesh is tetrahedralized to support finite element studies. The methods presented use convolutional neural networks (CNNs) that segment vertebrae from CT images to improve initial deformation alignment. In instances of CNN failure, methodological robustness, given an accurate segmentation, is demonstrated through the use of spinal columns which have been molded into a Lenke classification. Dice coefficient and Hausdorff distance metrics demonstrate the accuracy of the deformable model generation. Synthetically generated images are used for additional validation of soft tissue positioning. Quantitative results are highly competitive and qualitative interpretations suggest a strong level of accuracy and appropriate deformation. Soft tissue ground truths, present in synthetic data, provide further confirmation of accurate mesh generation. Following the completion of the methodological pipeline, accurate, patient-specific, anatomically inclusive models are ready for use in FE studies.https://digitalcommons.odu.edu/gradposters2021_engineering/1005/thumbnail.jp

Magnetic resonance imaging of the erector spinae muscles in Duchenne muscular dystrophy: implication for scoliotic deformities

<p>Abstract</p> <p>Background</p> <p>In Duchenne muscular dystrophy (DMD), the muscular degeneration often leads to the development of scoliosis. Our objective was to investigate how anatomical changes in back muscles can lead to scoliosis. Muscular volume and the level of fat infiltration in those muscles were thus evaluated, in non-scoliotic, pre-scoliotic and scoliotic patients. The overlying skin thickness over the apex level of scoliotic deformations was also measured to facilitate the interpretation of electromyographic signals when recorded on the skin surface.</p> <p>Methods</p> <p>In 8 DMD patients and two healthy controls with no known muscular deficiencies, magnetic resonance imaging (MRI) was used to measure continuously at 3 mm intervals the distribution of the erector spinae (ES) muscle in the T8-L4 region as well as fat infiltration in the muscle and overlying skin thickness: four patients were non-scoliotic (NS), two were pre-scoliotic (PS, Cobb angle < 15°) and two were scoliotic (S, Cobb angle ≥ 15°). For each subject, 63 images 3 mm thick of the ES muscle were obtained in the T8-L4 region on both sides of the spine. The pixel dimension was 0.39 × 0.39 mm. With a commercial software, on each 12 bits image, the ES contour on the left and on the right sides of the spine were manually determined as well as those of its constituents i.e., the iliocostalis (IL), the longissimus (LO) and the spinalis (SP) muscles. Following this segmentation, the surfaces within the contours were determined, the muscles volume were obtained, the amount of fat infiltration inside each muscle was evaluated and the overlying skin thickness measured.</p> <p>Findings</p> <p>The volume of the ES muscle of our S and PS patients was found smaller on the convex side relative to the concave one by 5.3 ± 0.7% and 2.8 ± 0.2% respectively. For the 4 NS patients, the volume difference of this muscle between right and left sides was 2.1 ± 1.5% and for the 2 controls, it was 1.4 ± 1.2%. Fat infiltration for the S and the PS patients was larger on the convex side than on the concave one (4.4 ± 1.6% and 4.5 ± 0.7% respectively) and the difference was more important near the apex. Infiltration was more important in the lateral IL muscle than in the medial SP and it was always larger near L2 than at any other spinal level. Fat infiltration was much more important in the ES for the DMD patients (49.9% ± 1.6%) than for the two controls (2.6 ± 0.8%). As for the overlying skin thickness measured near the deformity of the patients, it was larger on the concave than on the convex side: 14.8 ± 6.1 vs 13.5 ± 5.7 mm for the S and 10.3 ± 6.3 vs 9.8 ± 5.6 mm for the PS.</p> <p>Interpretation</p> <p>In DMD patients, our results indicate that a larger replacement of muscles fibers by fat infiltration on one side of the spine is a factor that can lead to the development of scoliosis. Efforts to slow such an infiltration on the most affected side of the spine could thus be beneficial to those patients by delaying the apparition of the scoliotic deformation. In addition to anatomical considerations, results obtained from the same patients but in experiments dealing with electromyography recordings, point to differences in the muscular contraction mechanisms and/or of the neural input to back muscles. This is similar to the adolescent idiopathic scoliosis (AIS) where a role of the nervous system in the development of the deformation has also been suggested.</p

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echogradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deeplearning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models

Personalized 3D reconstruction of the rib cage for clinical assessment of trunk deformities

Scoliosis is a 3D deformity of the spine and rib cage. Extensive validation of 3D reconstruction methods of the spine from biplanar radiography has already been published. In this article, we propose a novel method to reconstruct the rib cage, using the same biplanar views as for the 3D reconstruction of the spine, to allow clinical assessment of whole trunk deformities. This technique uses a semi-automatic segmentation of the ribs in the postero-anterior X-ray view and an interactive segmentation of partial rib edges in the lateral view. The rib midlines are automatically extracted in 2D and reconstructed in 3D using the epipolar geometry. For the ribs not visible in the lateral view, the method predicts their 3D shape. The accuracy of the proposed method has been assessed using data obtained from a synthetic bone model as a gold standard and has also been evaluated using data of real patients with scoliotic deformities. Results show that the reconstructed ribs enable a reliable evaluation of the rib axial rotation, which will allow a 3D clinical assessment of the spine and rib cage deformities.CIHR / IRS

Quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images

Background: For some scoliotic patients the spinal instrumentation is inevitable. Among these patients, those with stiff curvature will need thoracoscopic disk resection. The removal of the intervertebral disk with only thoracoscopic images is a tedious and challenging task for the surgeon. With computer aided surgery and 3D visualisation of the interverterbral disk during surgery, surgeons will have access to additional information such as the remaining disk tissue or the distance of surgical tools from critical anatomical structures like the aorta or spinal canal. We hypothesized that automatically extracting 3D information of the intervertebral disk from MR images would aid the surgeons to evaluate the remaining disk and would add a security factor to the patient during thoracoscopic disk resection.Methods: This paper presents a quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images. The automatic segmentation method is based on the watershed technique and morphological operators. The 3D Dice Similarity Coefficient (DSC) is the main statistical metric used to validate the automatically detected preoperative disk volumes. The automatic detections of intervertebral disks of real clinical MR images are compared to manual segmentation done by clinicians.Results: Results show that depending on the type of MR acquisition sequence, the 3D DSC can be as high as 0.79 (+/- 0.04). These 3D results are also supported by a 2D quantitative evaluation as well as by robustness and variability evaluations. The mean discrepancy (in 2D) between the manual and automatic segmentations for regions around the spinal canal is of 1.8 (+/- 0.8) mm. The robustness study shows that among the five factors evaluated, only the type of MRI acquisition sequence can affect the segmentation results. Finally, the variability of the automatic segmentation method is lower than the variability associated with manual segmentation performed by different physicians.Conclusions: This comprehensive evaluation of the automatic segmentation and 3D reconstruction of intervertebral disks shows that the proposed technique used with specific MRI acquisition protocol can detect intervertebral disk of scoliotic patient. The newly developed technique is promising for clinical context and can eventually help surgeons during thoracoscopic intervertebral disk resection

3D registration of MR and X-ray spine images using an articulated model

Présentation: Cet article a été publié dans le journal : Computerised medical imaging and graphics (CMIG). Le but de cet article est de recaler les vertèbres extraites à partir d’images RM avec des vertèbres extraites à partir d’images RX pour des patients scoliotiques, en tenant compte des déformations non-rigides due au changement de posture entre ces deux modalités. À ces fins, une méthode de recalage à l’aide d’un modèle articulé est proposée. Cette méthode a été comparée avec un recalage rigide en calculant l’erreur sur des points de repère, ainsi qu’en calculant la différence entre l’angle de Cobb avant et après recalage. Une validation additionelle de la méthode de recalage présentée ici se trouve dans l’annexe A. Ce travail servira de première étape dans la fusion des images RM, RX et TP du tronc complet. Donc, cet article vérifie l’hypothèse 1 décrite dans la section 3.2.1.Abstract This paper presents a magnetic resonance image (MRI)/X-ray spine registration method that compensates for the change in the curvature of the spine between standing and prone positions for scoliotic patients. MRIs in prone position and X-rays in standing position are acquired for 14 patients with scoliosis. The 3D reconstructions of the spine are then aligned using an articulated model which calculates intervertebral transformations. Results show significant decrease in regis- tration error when the proposed articulated model is compared with rigid registration. The method can be used as a basis for full body MRI/X-ray registration incorporating soft tissues for surgical simulation.Canadian Institute of Health Research (CIHR