Analyse automatique des images échographiques de la colonne vertébrale

Résumé L'échographie est une modalité d'imagerie médicale généralement utilisée pour visualiser les tissus mous dans différentes applications cliniques. Cette technique est très avantageuse étant donné son faible coût, sa portabilité et surtout l'absence de rayons ionisants pour former des images. Cependant, le contenu de ces images est complexe et peut être difficile à interpréter même pour un expert. De plus, afin d'obtenir des images échographiques exploitables, un positionnement adéquat de la sonde est nécessaire lors de l'acquisition. Ces deux inconvénients sont encore plus importants lors de l'acquisition d’images de structures osseuses. Ces structures réfléchissent entièrement les ondes ultrasonores, créant ainsi des surfaces très brillantes et des ombres acoustiques en dessous d'elles, rendant ainsi leur interprétation encore plus complexe. Dans le cas d’une vertèbre, la surface de l'apophyse épineuse est tellement petite que sa brillance est particulièrement dépendante de l'orientation et de la position de la sonde. D'autre part, la forme complexe de la vertèbre rend la frontière de son ombre acoustique plus difficile à définir. Cependant l'utilisation de l'échographie de la colonne vertébrale à la place de radiographies lors du suivi clinique de patients atteints de scoliose pourrait réduire le cumul de radiation. Plusieurs méthodes utilisant l'échographie ont été développées ces dernières années afin d’évaluer la scoliose ou de réajuster le corset pour des patients atteints de scoliose idiopathique adolescente (SIA). Ces méthodes requièrent des images de bonne qualité et une segmentation manuelle du contenu. Dans ce projet, nous proposons d’effectuer une analyse automatique des images échographiques vertébrales afin de comprendre le modèle de formation de ces images et de segmenter automatiquement les structures d’intérêt. Dans un premier temps, nous avons développé une méthode de segmentation automatique de l'apophyse épineuse et de l'ombre acoustique dans les images échographiques vertébrales afin d'aider l'utilisateur à interpréter ce type d'images. Cette méthode s'appuie, tout d'abord, sur l’extraction de différentes caractéristiques et leur validation afin de sélectionner l’ensemble le plus pertinent. Puis un classifieur est utilisé afin d'associer chaque pixel de l'image à une des trois régions suivantes : apophyse épineuse, ombre acoustique ou autres tissus. Finalement, une étape de régularisation est appliquée afin de prendre en compte les différentes propriétés des vertèbres. Nous avions une base de données contenant 181 images échographiques, mais nous n'en avons utilisé que 107, car seules celles-ci avaient une qualité acceptable. Un taux de classification de 84% pour l’apophyse épineuse et de 92% pour l’ombre acoustique ont été obtenus. De plus, le centroïde de l’apophyse épineuse segmentée se trouvait en moyenne à 0.38 mm de celui de la vérité terrain, provenant d’une segmentation manuelle validée par un radiologue. Nous avons aussi évalué la précision de la méthode proposée en comparant les régions segmentées automatiquement à celles délimitées manuellement et avons obtenu un coefficient de similarité DICE de 0.88 pour l’ombre acoustique et de 0.72 pour l’apophyse épineuse.----------Abstract Ultrasound (US) imaging is a medical imaging modality that is often used to visualize soft tissues in the human body in various clinical applications. This technique has several important advantages, in particular its low cost, portability, and the fact that it is radiation-free. However, the content of US images is rather complex and can be hard to interpret even for an expert. Furthermore, the quality of the content of US images will depend of the positioning of the probe during the acquisition. When measuring bone surfaces, these two disadvantages are accentuated. Indeed, the acoustic waves are entirely reflected by these hard structures, thereby creating bright surfaces with acoustic shadows below them, which make the interpretation of such images even more challenging. In the case of a vertebra, the surface of the spinous process is so small that its appearance in US images will strongly depend on the orientation and position of the probe. Moreover, it can be difficult to determine the boundary of the acoustic shadow created by the bone structure given the complicated shape of the vertebra. Nevertheless, in the clinical monitoring of scoliosis, using US images of the spine instead of X-rays could be very useful to reduce the cumulative radiation received by patients. In recent years, several methods using US imaging to evaluate scoliosis, or to adjust the brace in the treatment of adolescent idiopathic scoliosis (AIS), have been developed. These methods require good quality images and use manual segmentation of the image content. In this project, we propose a framework for the automatic analysis of US images of the spine (vertebrae) that utilizes an image formation model and an automatic segmentation of the regions of interest. First, we developed an automatic segmentation method to detect the spinous process and the acoustic shadow in the US images, aimed at helping the end user interpret the images. This method uses feature extraction and selection process in order to determine the most relevant set of features. The aim of the classification task is to discriminate three different regions: spinous process, acoustic shadow and other tissues. An LDA classifier is used to assign each image pixel to one of the three regions. Finally, we apply a regularization step which exploits several properties of vertebrae. Using a database of 107 US images, we obtained a classification rate of 84% for the spinous process and 92% for the acoustic shadow. In addition, the centroid of the automatically segmented spinous process was located 0.38 mm on average from that of the ground truth, as provided by a manual labelling that was validated by a radiologist. We also compared the automatically and manually segmented regions and obtained DICE similarity coefficients of 0.72 and 0.88 for the spinous process and acoustic shadow respectively

Development of ultrasound to measure deformation of functional spinal units in cervical spine

Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used.2020-02-20T00:00:00

The state-of-the-art in ultrasound-guided spine interventions.

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications

Identifying Visible Tissue in Intraoperative Ultrasound Images during Brain Surgery: A Method and Application

Intraoperative ultrasound scanning is a demanding visuotactile task. It requires operators to simultaneously localise the ultrasound perspective and manually perform slight adjustments to the pose of the probe, making sure not to apply excessive force or breaking contact with the tissue, whilst also characterising the visible tissue. In this paper, we propose a method for the identification of the visible tissue, which enables the analysis of ultrasound probe and tissue contact via the detection of acoustic shadow and construction of confidence maps of the perceptual salience. Detailed validation with both in vivo and phantom data is performed. First, we show that our technique is capable of achieving state of the art acoustic shadow scan line classification - with an average binary classification accuracy on unseen data of 0.87. Second, we show that our framework for constructing confidence maps is able to produce an ideal response to a probe's pose that is being oriented in and out of optimality - achieving an average RMSE across five scans of 0.174. The performance evaluation justifies the potential clinical value of the method which can be used both to assist clinical training and optimise robot-assisted ultrasound tissue scanning

Open-source software for ultrasound-based guidance in spinal fusion surgery.

Spinal instrumentation and surgical manipulations may cause loss of navigation accuracy requiring an efficient re-alignment of the patient anatomy with pre-operative images during surgery. While intra-operative ultrasound (iUS) guidance has shown clear potential to reduce surgery time, compared with clinical computed tomography (CT) guidance, rapid registration aiming to correct for patient misalignment has not been addressed. In this article, we present an open-source platform for pedicle screw navigation using iUS imaging. The alignment method is based on rigid registration of CT to iUS vertebral images and has been designed for fast and fully automatic patient re-alignment in the operating room. Two steps are involved: first, we use the iUS probe's trajectory to achieve an initial coarse registration; then, the registration transform is refined by simultaneously optimizing gradient orientation alignment and mean of iUS intensities passing through the CT-defined posterior surface of the vertebra. We evaluated our approach on a lumbosacral section of a porcine cadaver with seven vertebral levels. We achieved a median target registration error of 1.47 mm (100% success rate, defined by a target registration error <2 mm) when applying the probe's trajectory initial alignment. The approach exhibited high robustness to partial visibility of the vertebra with success rates of 89.86% and 88.57% when missing either the left or right part of the vertebra and robustness to initial misalignments with a success rate of 83.14% for random starts within ±20° rotation and ±20 mm translation. Our graphics processing unit implementation achieves an efficient registration time under 8 s, which makes the approach suitable for clinical application

Shape analysis for assessment of progression in spinal deformities

Adolescent idiopathic scoliosis (AIS) is a three-dimensional structural spinal deformation. It is the most common type of scoliosis. It can be visually detected as a lateral curvature in the postero-anterior plane. This condition starts in early puberty, affecting between 1-4% of the adolescent population between 10-18 years old, affecting in majority female. In severe cases (0.1% of population with AIS) the patient will require a surgical treatment. To date, the diagnosis of AIS relies on the quantification of the major curvature observed on posteroanterior and sagittal radiographs. Radiographs in standing position are the common imaging modality used in clinical settings to diagnose AIS. The assessment of the deformation is carried out using the Cobb angle method. This angle is calculated in the postero-anterior plane, and it is formed between a line drawn parallel to the superior endplate of the upper vertebra included in the scoliotic curve and a line drawn parallel to the inferior endplate of the lower vertebra of the same curve. Patients that present a Cobb angle of more than 10°, are diagnosed with AIS. The gold standard to classify curve deformations is the Lenke classification method. This paradigm is widely accepted in the clinical community. It divides spines with scoliosis into six types and provides treatment recommendations depending on the type. This method is limited to the analysis of the spine in the 2D space, since it relies on the observation of radiographs and Cobb angle measurements. On the one hand, when clinicians are treating patients with AIS, one of the main concerns is to determine whether the deformation will progress through time. Knowing beforehand of how the shape of the spine is going to evolve would aid to guide treatments strategies. On the other hand, however, patients at higher risks of progression require to be monitored more frequently, which results in constant exposure to radiation. Therefore, there is a need for an alternative radiation-free technology to reduce the use of radiographs and alleviate the perils of other health issues derived from current imaging modalities. This thesis presents a framework designed to characterize and model the variation of the shape of the spine throughout AIS. This framework includes three contributions: 1) two measurement techniques for computing 3D descriptors of the spine, and a classification method to categorize spine deformations, 2) a method to simulate the variation of the shape of the spine through time, and 3) a protocol to generate a 3D model of the spine from a volume reconstruction produced from ultrasound images. In our first contribution, we introduced two measurement techniques to characterize the shape of the spine in the 3D space, leave-n-out, and fan leave-n-out angles. In addition, a dynamic ensemble method was presented as an automated alternative to classify spinal deformations. Our measurement techniques were designed for computing the 3D descriptors and to be easy to use in a clinical setting. Also, the classification method contributes by assisting clinicians to identify patient-specific descriptors, which could help improving the classification in borderline curve deformations and, hence, suggests the proper management strategies. In order to observe how the shape of the spine progresses through time, in our second contribution, we designed a method to visualize the shape’s variation from the first visit up to 18 months, for every three months. Our method is trained with modes of variation, computed using independent component analysis from 3D model reconstructions of the spine of patients with AIS. Each of the modes of variation can be visualized for interpretation. This contribution could aid clinicians to identify which spine progression pattern might be prone to progression. Finally, our third contribution addresses the necessity of a radiation-free image modality for assessing and monitoring patients with AIS. We proposed a protocol to model a spine by identifying the spinous processes on a volume reconstruction. This reconstruction was computed from ultrasound images acquired from the external geometry of the subject. Our acquisition protocol documents a setup for image acquisition, as well as some recommendations to take into account depending on the body composition of the subjects to be scanned. We believe that this protocol could contribute to reduce the use of radiographs during the assessment and monitoring of patients with AIS

Artificial intelligence in musculoskeletal ultrasound imaging

Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.11Nsciescopu

Spinal stenosis

This thesis describes in detail the clinical spectrum of spinal stenosis in a series of two hundred and twenty-one patients at the Nuffield Orthopaedic Centre. It depicts those conditions with which spinal stenosis may be confused, and other conditions with which it is associated. Characteristic symptoms and physical signs are reported and the role and value of different methods of investigation are discussed. The aetiology and pathogenesis of spinal stenosis is discussed and the emphasis turned away from absolute measurements of the dimensions of the bony spinal canal, towards the role of the soft tissues and the dynamic response of the canal and its neural contents to postural change and loading, as evidenced by erect flexion and extension radiculography. The spinal reserve capacity measurement on CT approaches more closely the ideal of measurement of volumetric disproportion of canal and contents, but it takes no account of the dynamics of the canal. Magnetic Resonance Imaging may, in the future, provide the most objective criteria for diagnosis if section thickness can be reduced. Experimental spinal stenosis was produced in a group of immature New Zealand white rabbits. This was induced by sublaminar wiring at three levels at the age of eight weeks and allowing the animals to grow for twenty-four months before sacrifice and analysis of the spines. The effect of sublaminar wiring on the growth and development of the lamina and spinal canal was analysed using a Kontron Ibas Image Analysis Computer, and the results described and statistically analysed. The results of surgery were analysed in detail in a group of seventytwo patients with spinal stenosis at the Nuffield Orthopaedic Centre. The long-term results were compared with the initial post-operative result and two groups were identified: the stable result and the unstable result. The indications for and results of re-operation were also analysed in a group of twelve patients. Improved understanding of the aetiology of spinal stenosis has enhanced surgical management and results. The extent of surgical decompression must be precisely planned pre-operatively from radiographic and CT studies, and the surgeon must be able to execute this plan at operation. There is now no place for exploratory operations. The objective of surgery is adequate nerve root decompression without spinal de-stabilisation and when this is achieved, re-operation is redundant

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society