Automatic assessment of scoliosis surgery outcome on trunk shape using left-right trunk asymmetry.

PURPOSE: To present a novel set of Left-Right Trunk Asymmetry (LRTA) indices and use them to assess the postoperative appearance of the trunk in Adolescent Idiopathic Scoliosis (AIS) patients. METHODS: We hypothesize that LRTA measurements provide complementary information to existing trunk asymmetry indices when documenting the outcome of scoliosis surgery. Forty-nine AIS patients with thoracic curves who underwent posterior spinal fusion were included. All had surface topography scans taken preoperatively and at least 6 months postoperatively. We documented spinal curvature using Radiographic Cobb angles, scoliometer readings and coronal balance. To evaluate Global Trunk Asymmetry (GTA), we used the standard measures of Back Surface Rotation (BSR) and Trunk Lateral Shift (TLS). To measure LRTA, we identified asymmetry areas as regions of significant deviation between the left and right sides of the 3D back surface. New parameters called Deformation Rate (DR) and Maximum Asymmetry (MA) were measured in different regions based on the asymmetry areas. We compared the GTA and LRTA changes with those in spinal curvature before and after surgery. RESULTS: The GTA indices, mainly TLS, showed improvement for more than 75% of patients. There was significant improvement of LRTA in the shoulder blades and waist regions (95% and 80% of patients respectively). CONCLUSION: We report positive outcomes for LRTA in the majority of patients, specifically in the shoulder blades and waist, even when no reduction of BSR is observed. The proposed indices can evaluate local trunk asymmetries and the degree to which they are improved or worsened after scoliosis surgery.

To distinguish flexible and rigid lumbar curve from MRI texture analysis in adolescent idiopathic scoliosis: A feasibility study.

BACKGROUND: Imaging in side bending, supine, traction, fulcrum, and push prone are examples of methods used to evaluate the curve reduction of scoliotic spine. However, being able to determine spine curve flexibility from MRI would eliminate the need of additional X-ray radiation related to radiograph acquisition in side-bending. PURPOSE/HYPOTHESIS: To find specific texture features of lumbar postural muscles on MRI that can distinguish flexible from rigid lumbar scoliotic curves. We hypothesized that the changes occurring in postural muscles with scoliosis can be seen with MRI. STUDY TYPE: Retrospective study case control. POPULATION: With Institutional Review Board approval and informed consent, 15 adolescents with idiopathic scoliosis and scheduled for surgery were involved. FIELD STRENGTH/SEQUENCE: T1 -weighted MR images were performed on a 1.5T system using a spin echo sequence in the axial direction. ASSESSMENT: The spinal erector, quadratus lumborum and psoas major muscles were analyzed using textural features. STATISTICAL TESTS: Principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) were used to classify the lumbar postural muscles and calculate performance metrics. The lumbar flexibility index, measured from suspension tests, was used as ground truth measurement. RESULTS: The five discriminant features (out of 34 tested features) obtained from PCA were able to keep over 90% of the variability of the dataset. The right and left spinal erector and the left psoas major had the highest performance metrics to classify the spinal curve flexibility, with an accuracy over 0.80, a sensitivity over 0.82, a specificity over 0.68, and a Matthews correlation coefficient over 0.57. DATA CONCLUSION: This study analyzed MRI using texture information of muscle to distinguish flexible from rigid scoliotic curves. Some postural muscle such as the spinal erector and the psoas major are more likely to reflect the curve flexibility of a scoliotic participant. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017.

Robust universal nonrigid motion correction framework for first-pass cardiac MR perfusion imaging.

PURPOSE: To present and assess an automatic nonrigid image registration framework that compensates motion in cardiac magnetic resonance imaging (MRI) perfusion series and auxiliary images acquired under a wide range of conditions to facilitate myocardial perfusion quantification. MATERIALS AND METHODS: Our framework combines discrete feature matching for large displacement estimation with a dense variational optical flow formulation in a multithreaded architecture. This framework was evaluated on 291 clinical subjects to register 1.5T and 3.0T steady-state free-precession (FISP) and fast low-angle shot (FLASH) dynamic contrast myocardial perfusion images, arterial input function (AIF) images, and proton density (PD)-weighted images acquired under breath-hold (BH) and free-breath (FB) settings. RESULTS: Our method significantly improved frame-to-frame appearance consistency compared to raw series, expressed in correlation coefficient (R2 = 0.996 ± 3.735E-3 vs. 0.978 ± 2.024E-2, P < 0.0001) and mutual information (3.823 ± 4.098E-1 vs. 2.967 ± 4.697E-1, P < 0.0001). It is applicable to both BH (R2 = 0.998 ± 3.217E-3 vs. 0.990 ± 7.527E-3) and FB (R2 = 0.995 ± 3.410E-3 vs. 0.968 ± 2.257E-3) paradigms as well as FISP and FLASH sequences. The method registers PD images to perfusion T1 series (9.70% max increase in R2 vs. no registration, P < 0.001) and also corrects motion in low-resolution AIF series (R2 = 0.987 ± 1.180E-2 vs. 0.964 ± 3.860E-2, P < 0.001). Finally, we showed the myocardial perfusion contrast dynamic was preserved in the motion-corrected images compared to the raw series (R2 = 0.995 ± 6.420E-3). CONCLUSION: The critical step of motion correction prior to pixel-wise cardiac MR perfusion quantification can be performed with the proposed universal system. It is applicable to a wide range of perfusion series and auxiliary images with different acquisition settings. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1060-1072.

Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines.

Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the model through uncertainties. This paper presents a proof-of-concept for generalizing the aleatoric and epistemic uncertainties in unsupervised MR-CT synthesis of scoliotic spines. A novel adaptation of the cycle-consistency constraint in CycleGAN is proposed such that the model predicts the aleatoric uncertainty maps in addition to the standard volume-to-volume translation between Magnetic Resonance (MR) and Computed Tomography (CT) data. Ablation experiments were performed to understand uncertainty estimation as an implicit regularizer and a measure of the model's confidence. The aleatoric uncertainty helps in distinguishing between the bone and soft-tissue regions in CT and MR data during translation, while the epistemic uncertainty provides interpretable information to the user for downstream tasks.

Automatic 3D Lamina Curve Extraction from Freehand 3D Ultrasound Data using Sequential Localization Recurrent Convolutional Networks.

Freehand 3D ultrasound imaging is emerging as a promising modality for regular spine exams due to its non-invasiveness and affordability. The laminae landmarks play a critical role in depicting the 3D shape of the spine. However, the extraction of the 3D lamina curves from transverse ultrasound sequences presents a challenging task, primarily attributed to the presence of diverse contrast variations, imaging artifacts, the complex surface of vertebral bones, and the difficulties associated with probe manipulation. This paper proposes Sequential Localization Recurrent Convolutional Networks (SL-RCN), a novel deep learning model that takes the contextual relationships into account and embeds the transformation matrix feature as a 3D knowledge base to enhance accurate ultrasound sequence analysis. The assessment involved the analysis of 3D ultrasound sequences obtained from 10 healthy adult human participants, covering both the lumbar and thoracic regions. The performance of SL-RCN is evaluated through 7-fold cross-validation, employing the leave-one-participant-out strategy. The validity of the AI model training is assessed on test data from 3 participants. Normalized Discrete Fréchet Distance (NDFD) is employed as the main metric to evaluate the disparity of the extracted 3D lamina curves. In contrast to our previous 2D image analysis method, SL-RCN generates reduced left/right mean distance errors from 1.62/1.63mm to 1.41/1.40mm, and NDFDs from 0.5910/0.6389 to 0.4276/0.4567. The increase in the mean NDFD value from 7-fold cross-validation to the test-data experiment is less than 0.05. The experiments demonstrate the SL-RCN's capability in extracting accurate paired smooth lamina landmark curves.

Changes in trunk appearance following surgical correction of adolescent idiopathic scoliosis.

PURPOSE: To assess the postoperative appearance of the trunk in surgically treated scoliosis patients after a 2 year follow-up using reliable indices and compare the results with 6-month follow-up. METHODS: Forty-six Adolescent Idiopathic Scoliosis (AIS) patients (female; preop mean age 14.4 ± 2.4 years) who underwent a posterior spinal fusion from 2009 to 2018 were included in this study. All had Lenke 1A thoracic curves, with surface topography taken preoperatively, 6 months and 2 years postoperatively. To assess spinal deformity, we measured the proximal thoracic, main thoracic and thoracolumbar/lumbar Cobb angles in the frontal plane from spinal X-rays and inclinometer angles in the thoracic and lumbar regions. To assess trunk deformity, Back Surface Rotation (BSR) and Trunk Lateral Shift (TLS) were computed along the trunk. We analysed the effect of age, height, weight, Cobb angle, length of follow-up, and surgical technique. We also compared correction rates (CRs) of the spinal and trunk measurements after 6 months and 2 years. RESULTS: Good spinal correction was achieved, with Cobb angles decreasing in the whole cohort. CRs for TLS and BSR were positive (denoting improvement) for 76% and 48% of patients, respectively, after 2 years. Compared with 6 months, the mean TLS CR increased while there was no improvement for BSR on average. We found no significant association after 2 years between truncal index CRs and clinical variables (age, height, weight, preoperative Cobb angles) or surgical technique. However, there were significant correlations between the CRs of TLS and the main thoracic Cobb angle (r = 0.35), and between the CRs of BSR and thoracic inclinometer angle. CONCLUSION: Although more than 55% of the TLS was corrected after 2 years of follow-up, the BSR remained stable over time and the persistence of rib hump on the back surface could be observed. LEVEL OF EVIDENCE: III.

Robust unsupervised texture segmentation for motion analysis in ultrasound images.

PURPOSE: Ultrasound imaging has emerged as a promising cost-effective and portable non-irradiant modality for the diagnosis and follow-up of diseases. Motion analysis can be performed by segmenting anatomical structures of interest before tracking them over time. However, doing so in a robust way is challenging as ultrasound images often display a low contrast and blurry boundaries. METHODS: In this paper, a robust descriptor inspired from the fractal dimension is presented to locally characterize the gray-level variations of an image. This descriptor is an adaptive grid pattern whose scale locally varies as the gray-level variations of the image. Robust features are then located based on the gray-level variations, which are more likely to be consistently tracked over time despite the presence of noise. RESULTS: The method was validated on three datasets: segmentation of the left ventricle on simulated echocardiography (Dice coefficient, DC), accuracy of diaphragm motion tracking for healthy subjects (mean sum of distances, MSD) and for a scoliosis patient (root mean square error, RMSE). Results show that the method segments the left ventricle accurately ( DC = 0.84 ) and robustly tracks the diaphragm motion for healthy subjects ( MSD = 1.10 mm) and for the scoliosis patient ( RMSE = 1.22 mm). CONCLUSIONS: This method has the potential to segment structures of interest according to their texture in an unsupervised fashion, as well as to help analyze the deformation of tissues. Possible applications are not limited to US image. The same principle could also be applied to other medical imaging modalities such as MRI or CT scans.

Denoising OCT videos based on temporal redundancy.

The identification of eye diseases and their progression often relies on a clear visualization of the anatomy and on different metrics extracted from Optical Coherence Tomography (OCT) B-scans. However, speckle noise hinders the quality of rapid OCT imaging, hampering the extraction and reliability of biomarkers that require time series. By synchronizing the acquisition of OCT images with the timing of the cardiac pulse, we transform a low-quality OCT video into a clear version by phase-wrapping each frame to the heart pulsation and averaging frames that correspond to the same instant in the cardiac cycle. Here, we compare the performance of our one-cycle denoising strategy with a deep-learning architecture, Noise2Noise, as well as classical denoising methods such as BM3D and Non-Local Means (NLM). We systematically analyze different image quality descriptors as well as region-specific metrics to assess the denoising performance based on the anatomy of the eye. The one-cycle method achieves the highest denoising performance, increases image quality and preserves the high-resolution structures within the eye tissues. The proposed workflow can be readily implemented in a clinical setting.

MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy.

Reliable automatic diagnosis of Diabetic Retinopathy (DR) and Macular Edema (ME) is an invaluable asset in improving the rate of monitored patients among at-risk populations and in enabling earlier treatments before the pathology progresses and threatens vision. However, the explainability of screening models is still an open question, and specifically designed datasets are required to support the research. We present MAPLES-DR (MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy), which contains, for 198 images of the MESSIDOR public fundus dataset, new diagnoses for DR and ME as well as new pixel-wise segmentation maps for 10 anatomical and pathological biomarkers related to DR. This paper documents the design choices and the annotation procedure that produced MAPLES-DR, discusses the interobserver variability and the overall quality of the annotations, and provides guidelines on using the dataset in a machine learning context.

Multimodal image registration of the scoliotic torso for surgical planning.

BACKGROUND: This paper presents a method that registers MRIs acquired in prone position, with surface topography (TP) and X-ray reconstructions acquired in standing position, in order to obtain a 3D representation of a human torso incorporating the external surface, bone structures, and soft tissues. METHODS: TP and X-ray data are registered using landmarks. Bone structures are used to register each MRI slice using an articulated model, and the soft tissue is confined to the volume delimited by the trunk and bone surfaces using a constrained thin-plate spline. RESULTS: The method is tested on 3 pre-surgical patients with scoliosis and shows a significant improvement, qualitatively and using the Dice similarity coefficient, in fitting the MRI into the standing patient model when compared to rigid and articulated model registration. The determinant of the Jacobian of the registration deformation shows higher variations in the deformation in areas closer to the surface of the torso. CONCLUSIONS: The novel, resulting 3D full torso model can provide a more complete representation of patient geometry to be incorporated in surgical simulators under development that aim at predicting the effect of scoliosis surgery on the external appearance of the patient's torso.

Differences in standing and sitting postures of youth with idiopathic scoliosis from quantitative analysis of digital photographs.

The objective of this study was to explore whether differences in standing and sitting postures of youth with idiopathic scoliosis could be detected from quantitative analysis of digital photographs. Standing and sitting postures of 50 participants aged 10-20-years-old with idiopathic scoliosis (Cobb angle: 15° to 60°) were assessed from digital photographs using a posture evaluation software program. Based on the XY coordinates of markers, 13 angular and linear posture indices were calculated in both positions. Paired t-tests were used to compare values of standing and sitting posture indices. Significant differences between standing and sitting positions (p < 0.05) were found for head protraction, shoulder elevation, scapula asymmetry, trunk list, scoliosis angle, waist angles, and frontal and sagittal plane pelvic tilt. Quantitative analysis of digital photographs is a clinically feasible method to measure standing and sitting postures among youth with scoliosis and to assist in decisions on therapeutic interventions.

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays.

Purpose: Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can cause a dramatic drop in blood oxygen levels due to widespread lung inflammation. Chest radiography is widely used as a primary modality to detect ARDS due to its crucial role in diagnosing the syndrome, and the x-ray images can be obtained promptly. However, despite the extensive literature on chest x-ray (CXR) image analysis, there is limited research on ARDS diagnosis due to the scarcity of ARDS-labeled datasets. Additionally, many machine learning-based approaches result in high performance in pulmonary disease diagnosis, but their decisions are often not easily interpretable, which can hinder their clinical acceptance. This work aims to develop a method for detecting signs of ARDS in CXR images that can be clinically interpretable. Approach: To achieve this goal, an ARDS-labeled dataset of chest radiography images is gathered and annotated for training and evaluation of the proposed approach. The proposed deep classification-segmentation model, Dense-Ynet, provides an interpretable framework for automatically diagnosing ARDS in CXR images. The model takes advantage of lung segmentation in diagnosing ARDS. By definition, ARDS causes bilateral diffuse infiltrates throughout the lungs. To consider the local involvement of lung areas, each lung is divided into upper and lower halves, and our model classifies the resulting lung quadrants. Results: The quadrant-based classification strategy yields the area under the receiver operating characteristic curve of 95.1% (95% CI 93.5 to 96.1), which allows for providing a reference for the model's predictions. In terms of segmentation, the model accurately identifies lung regions in CXR images even when lung boundaries are unclear in abnormal images. Conclusions: This study provides an interpretable decision system for diagnosing ARDS, by following the definition used by clinicians for the diagnosis of ARDS from CXR images.

A Web-Based Platform for the Automatic Stratification of ARDS Severity.

Acute respiratory distress syndrome (ARDS), including severe pulmonary COVID infection, is associated with a high mortality rate. It is crucial to detect ARDS early, as a late diagnosis may lead to serious complications in treatment. One of the challenges in ARDS diagnosis is chest X-ray (CXR) interpretation. ARDS causes diffuse infiltrates through the lungs that must be identified using chest radiography. In this paper, we present a web-based platform leveraging artificial intelligence (AI) to automatically assess pediatric ARDS (PARDS) using CXR images. Our system computes a severity score to identify and grade ARDS in CXR images. Moreover, the platform provides an image highlighting the lung fields, which can be utilized for prospective AI-based systems. A deep learning (DL) approach is employed to analyze the input data. A novel DL model, named Dense-Ynet, is trained using a CXR dataset in which clinical specialists previously labelled the two halves (upper and lower) of each lung. The assessment results show that our platform achieves a recall rate of 95.25% and a precision of 88.02%. The web platform, named PARDS-CxR, assigns severity scores to input CXR images that are compatible with current definitions of ARDS and PARDS. Once it has undergone external validation, PARDS-CxR will serve as an essential component in a clinical AI framework for diagnosing ARDS.

Evaluation of the SIMULRESP: A simulation software of child and teenager cardiorespiratory physiology.

BACKGROUND: Mathematical models based on the physiology when programmed as a software can be used to teach cardiorespiratory physiology and to forecast the effect of various ventilatory support strategies. We developed a cardiorespiratory simulator for children called "SimulResp." The purpose of this study was to evaluate the quality of SimulResp. METHODS: SimulResp quality was evaluated on accuracy, robustness, repeatability, and reproducibility. Blood gas values (pH, PaCO2 , PaO2,  and SaO2 ) were simulated for several subjects with different characteristics and in different situations and compared to expected values available as reference. The correlation between reference and simulated data was evaluated by the coefficient of determination and Intraclass correlation coefficient. The agreement was evaluated with the Bland & Altman analysis. RESULTS: SimulResp produced healthy child physiological values within normal range (pH 7.40 ± 0.5; PaCO2 40 ± 5 mmHg; PaO2 90 ± 10 mmHg; SaO2 97 ± 3%) starting from a weight of 25-35 kg, regardless of ventilator support. SimulResp failed to simulate accurate values for subjects under 25 kg and/or affected with pulmonary disease and mechanically ventilated. Based on the repeatability was considered as excellent and the reproducibility as mild to good. SimulResp's prediction remains stable within time. CONCLUSIONS: The cardiorespiratory simulator SimulResp requires further development before future integration into a clinical decision support system.

Toward the next generation of simulator for intraoperative navigation of scoliotic spine surgeries.

Surgical navigation systems are useful for planning pedicle screw positioning and guiding drilling trajectories. However, it is not yet possible to intraoperatively predict the correction of the scoliotic spine resulting from specific screw and rod configuration and instrumentation maneuvers. In this context, the objective of this study is to develop a novel intraoperative simulator for navigated scoliotic spine surgeries. An instrumentation strategy (pedicle screw insertion, rod attachment and rotation, set screw tightening) was computationally simulated on a synthetic model of a scoliotic spine using the preoperative radiographs in the standing position and various parameters recreating the preoperative conditions. The intraoperative decubitus position was then simulated. The resulting geometry was identified using a navigation system and transferred to the simulator, which enabled the updating of the preoperative planning, computing of clinical indices (Cobb angles, etc.) and simulation of instrumentation maneuvers. The Cobb angle decreased from 34° to 24° between the simulated pre- and intraoperative spine (before the instrumentation). Difference in pedicle screw positioning between the preoperative planning and the intraoperative situation was less than 0.5 mm. The intraoperative simulation of the rod attachment and rotation maneuvers resulted in a 12° Cobb angle. In conclusion, this preliminary study is a first step toward developing an integrated simulator for preoperative planning and intraoperative navigation of scoliotic spine surgeries. Once completed, the new intraoperative simulator will enable the surgeon to obtain real-time biomechanical feedback during the navigated surgery of a scoliotic spine, and may contribute to improve the resulting correction and instrumentation parameters (instrumented levels, surgical maneuvers, generated forces, etc.).

Focused Attention in Transformers for interpretable classification of retinal images.

Vision Transformers have recently emerged as a competitive architecture in image classification. The tremendous popularity of this model and its variants comes from its high performance and its ability to produce interpretable predictions. However, both of these characteristics remain to be assessed in depth on retinal images. This study proposes a thorough performance evaluation of several Transformers compared to traditional Convolutional Neural Network (CNN) models for retinal disease classification. Special attention is given to multi-modality imaging (fundus and OCT) and generalization to external data. In addition, we propose a novel mechanism to generate interpretable predictions via attribution maps. Existing attribution methods from Transformer models have the disadvantage of producing low-resolution heatmaps. Our contribution, called Focused Attention, uses iterative conditional patch resampling to tackle this issue. By means of a survey involving four retinal specialists, we validated both the superior interpretability of Vision Transformers compared to the attribution maps produced from CNNs and the relevance of Focused Attention as a lesion detector.

Mapping Pulsatile Optic Nerve Head Deformation Using OCT.

Objective: To develop a noninvasive technique to quantitatively assess the pulsatile deformation due to cardiac contractions of the optic nerve head (ONH). Design: Evaluation of a diagnostic test or technology. Participants: Healthy subjects with no history of refractive surgery, divided into 2 cohorts on the basis of their axial length (AL). Methods: We present a noninvasive technique to quantitatively assess the pulsatile deformation of the ONH tissue by combining high-frequency OCT imaging and widely available image processing algorithms. We performed a thorough validation of the approach, numerically and experimentally, evaluating the sensitivity of the method to artificially induced deformation and its robustness to different noise levels. We performed deformation measurements in cohorts of healthy (n = 9) and myopic (n = 5) subjects in different physiological strain conditions by calculating the amplitude of tissue displacement in both the primary position and abduction. The head rotation was measured using a goniometer. During imaging in abduction, the head was rotated 40° ± 3°, and subjects were instructed to direct their gaze toward the OCT visual target. Main Outcome Measures: Pulsatile tissue displacement maps. Results: The robustness of the method was assessed using artificial deformations and increasing noise levels. The results show acceptable absolute errors before the noise simulations grossly exaggerate image degradation. For the group of subjects with AL of < 25 mm (n = 9), the median pulsatile displacement of the ONH was 7.8 ± 1.3 µm in the primary position and 8.9 ± 1.2 µm in abduction. The Wilcoxon test showed a significant difference (P ≤ 0.005) between the 2 paired measures. Reproducibility was tested in 2 different sessions in 5 different subjects with the same intraocular pressure, and an intraclass correlation coefficient of 0.99 was obtained (P < 0.005). Conclusions: The computational pipeline demonstrated good reproducibility and had the capacity to accurately map the pulsatile deformation of the optic nerve. In a clinical setting, we detected physiological changes in normal subjects supporting its translation potential as a novel biomarker for the diagnosis and progression of optic nerve diseases.

Time series spinal radiographs as prognostic factors for scoliosis and progression of spinal deformities.

The effectiveness of clinical measures to predict scoliotic progression is unclear. The objective of this study was to identify potential prognostic factors affecting scoliosis progression. Consecutive measurements (181) from 35 non-instrumented adolescent idiopathic scoliosis patients with at least two follow-up assessments were studied. Potential prognostic factors of gender, curve pattern, age, curve magnitude, apex location and lateral deviation and spinal growth were analyzed. Stable and progressed groups were compared (threshold: Cobb angle ≥5° or 10°) with sequential clinical data collected in 6-month intervals. Double curves progressed simultaneously or alternatively on curve regions. Age was not significantly different prior to and at maximal Cobb angle. Maximal Cobb angles were significantly correlated to initial Cobb angles (r = 0.81-0.98). Progressed males had larger initial Cobb angles than progressed females. Apex locations were higher in progressed than stable groups, and at least a half vertebra level higher in females than males. Maximal apex lateral deviations correlated significantly with the initial ones (r = 0.73-0.97) and moderately with maximal Cobb angles (r = 0.33-0.85). In the progressed groups, males had larger apex lateral deviations than females. Spinal growth did not relate to curve progression (r = -0.64 to +0.59) and was not significantly different between groups and genders. Scoliosis may dynamically progress between major and minor curves. Gender, curve magnitude, apex location and lateral deviation have stronger effects on scoliosis progression than age or spinal growth. Females with high apex locations may be expected to progress.

[Automatic extraction of vertebral landmarks from ultrasound images]. / Extraction automatique de repères vertébraux à partir d'échographies.

Some forms of myopathies such as Duchenne muscular dystrophy cause a progressive degeneration of the patient's muscles. This results in the development of scoliosis, which increases in severity over time. The clinical standard for monitoring scoliosis is to perform an X-ray on a regular basis. Unfortunately, repeated exposure to X-rays is harmful to the patient's health. Ultrasound imaging is a radiation-free modality that uses ultrasound (US) waves. However, the interpretation of vertebral ultrasound images is often difficult due to the variable quality of the image. In order to tackle this challenge, we present a method to localize the vertebrae on US images automatically. The validation of this reproducible approach suggests that it would be possible, in the long term, to replace part of the X-ray exams by US imaging.

TITLE: Extraction automatique de repères vertébraux à partir d'échographies. ABSTRACT: Certaines formes de myopathies telles que la dystrophie musculaire de Duchenne entraînent une dégénérescence progressive des muscles chez le patient. Ceci se traduit par l'apparition d'une scoliose dont la gravité augmente au cours du temps. La norme clinique pour le suivi de la scoliose consiste à réaliser un examen radiographique. Malheureusement, l'exposition répétée aux rayons X est nocive pour la santé du patient. L'échographie est une technique d'imagerie médicale non irradiante qui utilise des ondes ultrasonores (US). Cependant, l'interprétation des échographies de vertèbres est souvent difficile en raison de la qualité variable des images. En réponse à ce défi, nous présentons une méthode pour localiser automatiquement les vertèbres sur les échographies. La validation de cette approche reproductible laisse à penser qu'il serait possible, à terme, de remplacer une partie des examens radiographiques standards par l'échographie.

Atlas-based score for automatic glaucoma risk stratification.

Glaucoma is a disease that affects the optic nerve and can lead to blindness. The cup-to-disc ratio (CDR) measurement is one of the key clinical indicators for glaucoma assessment. However, the CDR only evaluates the relative sizes of the cup and optic disc (OD) via their diameters, and does not characterize local morphological changes that can inform clinicians on early signs of glaucoma. In this work, we propose a novel glaucoma score based on a statistical atlas framework that automatically quantifies the deformations of the OD region induced by glaucoma. A deep-learning approach is first used to segment the optic cup with a dedicated atlas-based data augmentation strategy. The segmented OD region (disc, cup and vessels) is then registered to the statistical OD atlas and the deformation is projected onto the atlas eigenvectors. The atlas glaucoma score (AGS) is then obtained by a linear combination of the principal modes of deformation of the atlas with linear discriminant analysis. The AGS performs better than the CDR on the three datasets used for evaluation, including RIM-ONE and ORIGA650. Compared to the CDR measurement, which yields an area under the ROC curve (AUC) of 91.4% using the expert segmentations, the AGS achieves an AUC of 98.2%. Our novel glaucoma score captures more complex deformations within the optic disc region than the CDR can. Such morphological changes are the first cue of glaucoma onset, before the visual field is affected. The proposed approach can thus significantly improve early detection of glaucoma.