Fully automatic cervical vertebrae segmentation framework for X-ray images

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved

Fully automatic image analysis framework for cervical vertebra in X-ray images

Despite the advancement in imaging technologies, a fifth of the injuries in the cervical spine remain unnoticed in the X-ray radiological exam. About a two-third of the subjects with unnoticed injuries suffer tragic consequences. Based on the success of computer-aided systems in several medical image modalities to enhance clinical interpretation, we have proposed a fully automatic image analysis framework for cervical vertebrae in X-ray images. The framework takes an X-ray image as input and highlights different vertebral features at the output. To the best of our knowledge, this is the first fully automatic system in the literature for the analysis of the cervical vertebrae. The complete framework has been built by cascading specialized modules, each of which addresses a specific computer vision problem. This dissertation explores data-driven supervised machine learning solutions to these problems. Given an input X-ray image, the first module localizes the spinal region. The second module predicts vertebral centers from the spinal region which are then used to generate vertebral image patches. These patches are then passed through machine learning modules that detect vertebral corners, highlight vertebral boundaries, segment vertebral body and predict vertebral shapes. In the process of building the complete framework, we have proposed and compared different solutions to the problems addressed by each of the modules. A novel region-aware dense classification deep neural network has been proposed for the first module to address the spine localization problem. The proposed network outperformed the standard dense classification network and random forestbased methods. Location of the vertebral centers and corners vary based on human interpretation and thus are better represented by probability maps than single points. To learn the mapping between the vertebral image patches and the probability maps, a novel neural network capable of predicting a spatially distributed probabilistic distribution has been proposed. The network achieved expert-level performance in localizing vertebral centers and outperform the Harris corner detector and Hough forest-based methods for corner localization. The proposed network has also shown its capability for detecting vertebral boundaries and produced visually better results than the dense classification network-based boundary detectors. Segmentation of the vertebral body is a crucial part of the proposed framework. A new shapeaware loss function has been proposed for training a segmentation network to encourage prediction of vertebra-like structures. The segmentation performance improved significantly, however, the pixel-wise nature of proposed loss function was not able to constrain the predictions adequately. To solve the problem a novel neural network was proposed which predicts vertebral shapes and trains on a loss function defined in the shape space. The proposed shape predictor network was capable of learning better topological information about the vertebra than the shape-aware segmentation network. The methods proposed in this dissertation have been trained and tested on a challenging dataset of X-ray images collected from medical emergency rooms. The proposed, first-of-its-kind, fully automatic framework produces state-of-the-art results both quantitatively and qualitatively

Spinal cord volume quantification and clinical application in multiple sclerosis

Magnetic resonance imaging of the spinal cord is a valuable part of the diagnostic work-up in patients with multiple sclerosis and other neurological disorders. Currently, mainly signal intensity changes within the cord in MR-images are considered in the clinical management of disorders of the central nervous system. However, cross-sectional or longitudinal measurements of spinal cord volume may deliver additional valuable information. Hence, the overall goal of this doctoral thesis was twofold: i) to clinically validate methods for quantification of spinal cord volume and spinal cord compartments, which are suitable for longitudinal assessment of large patient cohorts and clinical practice and ii) to evaluate spinal cord volume as a potential valuable biomarker and provide new insights into the role of spinal cord damage in multiple sclerosis. The first part focuses on the validation of quantification methods for spinal cord volume and includes two projects. While several MRI-based approaches of semi- and fully automatic techniques for volumetric spinal cord measurements have been proposed, up to now no gold standard exists and only a few methods have been validated and/or evaluated on patient follow-up scans to demonstrate their applicability in longitudinal settings. One of the latter segmentation methods was recently developed in-house and showed excellent reliability for cervical cord segmentation (Cordial, the cord image analyzer). In a first project, we extended its applicability to the lumbar cord, since no other software has been tested so far within this anatomical region of interest. On a well-selected dataset of 10 healthy controls (scanned in a scan-rescan fashion) we were able to show that - even within this technically challenging region - this segmentation algorithm provides excellent inter- and intra-session reproducibility showing high potential for application in longitudinal trials. In a second project, we aimed at obtaining volumetric information on particular compartments of the spinal cord such as the cord grey and white matter, since recent studies in multiple sclerosis provided evidence that measuring spinal cord grey matter volume changes may be a better biomarker for disease progression than quantifying cord white matter pathology or even volumetric brain measures. We therefore implemented a novel imaging approach, the averaged magnetization inversion recovery acquisitions sequence, for better grey and white matter visualization within the cord and scanned 24 healthy controls in a scan-rescan fashion. Further we applied an innovative fully automatic variational segmentation algorithm with a shape prior modified for 3D data with a slice similarity prior to segment the spinal cord grey and white matter. This pipeline allowed for highly accurate and reproducible grey and white matter segmentation within the cord. In view of its features, our automatic segmentation method seems promising for further application in both cross-sectional and longitudinal and in large multi-center studies. The second goal of this thesis was the clinical application of the above-mentioned methods for the evaluation of spinal cord volume changes as a potential biomarker in multiple sclerosis patients. For this purpose, we quantified spinal cord volume change in a large cohort of 243 multiple sclerosis patients, followed over a period of 6 years with annual clinical and MRI examinations. Spinal cord volume proved to be a strong predictor of physical disability and disease progression, indicating that it may be a suitable marker for monitoring disease activity and severity in all disease types but especially in progressive multiple sclerosis. Spinal cord volume also proved to be the only MRI metric to strongly explain the clinical progression over time as opposed to brain atrophy and lesion measures

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research