Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

The Health of Children and Young People with Chronic Conditions and Disabilities in New Zealand 2016

This report aims to assist district health boards to plan to meet current and future demands in order to improve the quality of life for children with disabilities and chronic conditions by providing: 1. Information from a range of routinely collected data on children and young people’s disability and chronic conditions, including prevalence of conditions arising in the perinatal period 2. Information about children’s and young people’s use of secondary health services 3. Evidence for good practice derived from current policies, guidelines and evidence-based interventions for each of the indicators presented The choice of indicators included in this report was informed by an indicator framework developed by the NZ Child and Youth Epidemiology Service and by recent peer-reviewed literature about chronic conditions in children and young people. Chronic conditions and disabilities often affect people for life. Having a good quality of life and flourishing to your best ability is dependent, at least in part, on what happened as you were growing up. Understanding the dimensions of chronic conditions and disabilities among children and young people is essential to planning and developing good quality health services for New Zealand’s children and young people

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

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Prenatal Diagnosis

This book provides detailed and comprehensive coverage on various aspects of prenatal diagnosis-with particular emphasis on sonographic and molecular diagnostic issues. It features sections dedicated to fundamentals of clinical, ultrasound and genetics diagnosis of human diseases, as well as current and future health strategies related to prenatal diagnosis. This book highlights the importance of utilizing fetal ultrasound/clinical/genetics knowledge to promote and achieve optimal health in fetal medicine. It will be a very useful resource to practitioners and scientists in fetal medicine

Camera- and Viewpoint-Agnostic Evaluation of Axial Postural Abnormalities in People with Parkinson’s Disease through Augmented Human Pose Estimation

Axial postural abnormalities (aPA) are common features of Parkinson’s disease (PD) and manifest in over 20% of patients during the course of the disease. aPA form a spectrum of functional trunk misalignment, ranging from a typical Parkinsonian stooped posture to progressively greater degrees of spine deviation. Current research has not yet led to a sufficient understanding of pathophysiology and management of aPA in PD, partially due to lack of agreement on validated, user-friendly, automatic tools for measuring and analysing the differences in the degree of aPA, according to patients’ therapeutic conditions and tasks. In this context, human pose estimation (HPE) software based on deep learning could be a valid support as it automatically extrapolates spatial coordinates of the human skeleton keypoints from images or videos. Nevertheless, standard HPE platforms have two limitations that prevent their adoption in such a clinical practice. First, standard HPE keypoints are inconsistent with the keypoints needed to assess aPA (degrees and fulcrum). Second, aPA assessment either requires advanced RGB-D sensors or, when based on the processing of RGB images, they are most likely sensitive to the adopted camera and to the scene (e.g., sensor–subject distance, lighting, background–subject clothing contrast). This article presents a software that augments the human skeleton extrapolated by state-of-the-art HPE software from RGB pictures with exact bone points for posture evaluation through computer vision post-processing primitives. This article shows the software robustness and accuracy on the processing of 76 RGB images with different resolutions and sensor–subject distances from 55 PD patients with different degrees of anterior and lateral trunk flexion