The robustness and accuracy of the intensity-based 3D/2D registration of a 3D model on planar X-ray image(s) is related to the quality of the image correspondences between the digitally reconstructed radiographs (DRR) generated from the 3D models (varying image) and the X-ray images (fixed target). While much effort may be devoted to generating realistic DRR that are similar to real X-rays (using complex X-ray simulation, adding densities information in 3D models, etc.), significant differences still remain between DRR and real X-ray images. Differences such as the presence of adjacent or superimposed soft tissue and bony or foreign structures lead to image matching difficulties and decrease the 3D/2D registration performance. In the proposed method, the X-ray images were converted into DRR images using a GAN-based cross-modality image-to-images translation. With this added prior step of XRAY-to-DRR translation, standard similarity measures become efficient even when using simple and fast DRR projection. For both images to match, they must belong to the same image domain and essentially contain the same kind of information. The XRAY-to-DRR translation also addresses the well-known issue of registering an object in a scene composed of multiple objects by separating the superimposed or/and adjacent objects to avoid mismatching across similar structures. We applied the proposed method to the 3D/2D fine registration of vertebra deformable models to biplanar radiographs of the spine. We showed that the XRAY-to-DRR translation enhances the registration results, by increasing the capture range and decreasing dependence on the similarity measure choice since the multi-modal registration becomes mono-modal.

To date, 3D spine reconstruction from biplanar radiographs involves intensive user supervision and semi-automated methods that are time-consuming and not effective in clinical routine. This paper proposes a new, fast, and automated 3D spine reconstruction method through which a realistic statistical shape model of the spine is fitted to images using convolutional neural networks (CNN). The CNNs automatically detect the anatomical landmarks controlling the spine model deformation through a hierarchical and gradual iterative process. The performance assessment used a set of 68 biplanar radiographs, composed of both asymptomatic subjects and adolescent idiopathic scoliosis patients, in order to compare automated reconstructions with ground truths build using multiple experts-supervised reconstructions. The mean (SD) errors of landmark locations (3D Euclidean distances) were 1.6 (1.3) mm, 1.8 (1.3) mm, and 2.3 (1.4) mm for the vertebral body center, endplate centers, and pedicle centers, respectively. The clinical parameters extracted from the automated 3D reconstruction (reconstruction time is less than one minute) presented an absolute mean error between 2.8° and 4.7° for the main spinal parameters and between 1° and 2.1° for pelvic parameters. Automated and expert's agreement analysis reported that, on average, 89% of automated measurements were inside the expert's confidence intervals. The proposed automated 3D spine reconstruction method provides an important step that should help the dissemination and adoption of 3D measurements in clinical routine.

An enhanced version of a segmentation algorithm applied in X‐ray images using a prior shape and a straightened boundary image (SBI) is proposed. In the SBI method, the boundary of the target object is extracted with a constant width along the prior shape and transformed to a rectangular image in which the edges are straightened. A new minimal path algorithm is proposed and applied to SBI minimising a cost function to select the best path corresponding to the edges of the target object. The cost function is calculated based on all possible paths from each pixel to the beginning of the image while lowering the computational complexity. Comparing with previous methods, the proposed method removes artefacts and provides clearer and smoother edges even when the prior shape is far from the target object. The method is also less sensitive to the initial positioning of the prior shape model.

Bony structure segmentation in radiographic images is an important tool for the diagnosis and treatment of orthopedic conditions. Current methods rely on the detection of single edges which often fail to correctly recover a structure's boundary when other nearby structures are present, as is the case in the hip joint. The use of minimal paths to detect separately two adjacent edges may lead to leakage of the femoral head contour into the acetabulum's edge due to small intensity variations and/or edge discontinuity. This article presents a new method for simultaneously detecting two adjacent edges in a radiographic image by using a novel 3D minimal path algorithm where interrelation constraints are incorporated. We apply this technique on radiographic images of hip joint in order to simultaneously extract adjacent bony contours of femoral head and acetabulum. We prove that the new algorithm improves the extraction of both contours in the region.

The fiber bundle density (FBD) calculated from ultrasound B-scan images of the equine superficial digital flexor tendon (SDFT) can serve as an objective measurement to characterize the three metacarpal sites of normal SDFTs, and also to discriminate a healthy SDFT from an injured one. In this paper, we propose a shock filter algorithm for the thinning of hyper-echoic structures observed in B-scan images of the SDFT. This algorithm is further enhanced by applying closing morphological operations on filtered images to facilitate extraction and quantification of fiber bundle fascicles. The mean FBD values were calculated from a clinical B-scan image dataset of eight normal and five injured SDFTs. The FBD values measured at three different tendon sites in normal cases show a highest density on the proximal site (five cases out of eight) and a lowest value on the distal part (seven cases out of eight). The mean FBD values measured on the entire tendon from the whole B-scan image dataset show a significant difference between normal and injured SDFTs: 51 (±9) for the normal SDFTs and 39 (±7) for the injured ones (p = 0.004) . This difference likely indicates disruption of some fiber fascicle bundles where lesions occurred. To conclude, the potential of this imaging technique is shown to be efficient for anatomical structural SDFT characterizations, and opens the way to clinically identifying the integrity of SDFTs.

Surgical instrumentation for the Adolescent idiopathic scoliosis (AIS) is a complex procedure involving many difficult decisions. Selection of the appropriate fusion level remains one of the most challenging decisions in scoliosis surgery. Currently, the Lenke classification model is generally followed in surgical planning. The purpose of our study is to investigate a computer aided method for Lenke classification and scoliosis fusion level selection. The method uses a self organizing neural network trained on a large database of surgically treated AIS cases. The neural network produces two maps, one of Lenke classes and the other of fusion levels. These two maps show that the Lenke classes are associated with the the proper fusion level categories everywhere in the map except at the Lenke class transitions. The topological ordering of the Cobb angles in the neural network justifies determining a patient scoliotic treatment instrumentation using directly the fusion level map rather than via the Lenke classification.

In this paper, we present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers. This method is based on the stochastic version of the expectation maximization (SEM) algorithm. Parameters of this mixture model are herein used to constrain the 3D reconstruction problem of scoliotic rib cage from a pair of planar and conventional calibrated radiographic images (postero-anterior with normal incidence (I/sub PA/) and lateral (I/sub LAT/)). More precisely, the proposed PPCA mixture model is herein robustly exploited for dimensionality reduction and to get a set of probabilistic prior models associated to each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood and for each considered class-conditional prior model, the proposed 3D reconstruction is stated as an energy function minimization problem, which is solved with a stochastic optimization algorithm. The optimal 3D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3D method of reconstruction has been successfully tested on several biplanar radiographic images, yielding very promising results.

We have developed a method to study the statistical properties of the noise found in various medical images. The method is specifically designed for types of noise with uncorrelated fluctuations. Such signal fluctuations generally originate in the physical processes of imaging rather than in the tissue textures. Various types of noise (e.g., photon, electronics, and quantization) often contribute to degrade medical images; the overall noise is generally assumed to be additive with a zero-mean, constant-variance Gaussian distribution. However, statistical analysis suggests that the noise variance could be better modeled by a nonlinear function of the image intensity depending on external parameters related to the image acquisition protocol. We present a method to extract the relationship between an image intensity and the noise variance and to evaluate the corresponding parameters. The method was applied successfully to magnetic resonance images with different acquisition sequences and to several types of X-ray images.

In this paper, we propose a new and accurate 3D reconstruction technique for the scoliotic spine from a pair planar and conventional radiographic images (postero-anterior and lateral). The proposed model uses a priori hierarchical global knowledge, both on the geometric structure of the whole spine and of each vertebra. More precisely, it relies on the specification of two 3D templates. The first, a rough geometric template on which rigid admissible deformations are defined, is used to ensure a crude registration of the whole spine. 3D reconstruction is then refined for each vertebra, by a template on which nonlinear admissible global deformations are modeled, with statistical modal analysis of the pathological deformations observed on a representative scoliotic vertebra population. This unsupervised coarse-to-fine 3D reconstruction procedure is stated as a double energy function minimization problems efficiently solved with a stochastic optimization algorithm. The proposed method, tested on several pairs of biplanar radiographic images with scoliotic deformities, is comparable in terms of accuracy with the classical CT-scan technique while being unsupervised and requiring a lower amount of radiation for the patient.

This paper proposes a multi-resolution analysis (MRA) approach to vertebral body segmentation in digital radiographs. A wavelet-based technique carries out the extraction of the most significant features in the radiographs. It generates a multi-resolution edge characterization, discarding noisy artifacts in the meantime. Specific features classification and geometric alignment is then performed, using statistical data to build the. models and curvature analysis to sort the edges. An active contour scheme generates the final contours to be used in clinical studies of the spine.

This paper presents a new 3D reconstruction method of the scoliotic vertebrae of a spine, using two conventional radiographic views (postero-anterior and lateral), and global prior knowledge on the geometrical structure of each vertebra. This geometrical knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in the Karhunen-Loeve expansion of the pathological deformations observed on a representative scoliotic vertebra population. The proposed reconstruction method consists in fitting the projections of this deformable template with the segmented contours of the corresponding vertebra on the two radiographic views. The 3D reconstruction problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. The reconstruction of the spine is then made vertebra by vertebra. This 3D reconstruction method has been successfully tested on several biplanar radiographic images, yielding very promising results.

The main objective of this study was to develop a 3D reconstruction technique of the spine and rib cage of idiopathic scoliotic patients using the self-calibration of the imaging system. The proposed approach computes the intrinsic and extrinsic parameters of the radiographic setup with respect to the global coordinate system used at Ste-Justine Hospital. Our approach determines an optimal estimate of the geometrical parameters of the imaging system from a nonlinear minimization of the mean square distance between the observed and analytical projections of a set of matched points identified on a pair of radiographic views. The accuracy of the optimal estimate for the intrinsic parameters was significantly improved when geometric knowledge such as the known length of detectable straight bars is incorporated as a set of equality constraints in the optimization process. Furthermore, in order to retrieve the 3D structure of interest in the global coordinate system, a reference plane including the origin of the global coordinate system is specified. Computer simulations were performed to evaluate the self-calibration procedure and to determine the minimum knowledge required to obtain an accurate 3D reconstruction for clinical applications. An in vitro validation on real images of a dry cadaveric human spine showed that the method is feasible and reaches the expected accuracy.