Heart rate estimation from ballistocardiographic signals using deep learning.

Ballistocardiography (BCG) is an unobtrusive approach for cost-effective and patient-friendly health monitoring. In this work, deep learning methods are used for heart rate estimation from BCG signals and are compared against five digital signal processing methods found in literature.The models are evaluated on a dataset featuring BCG recordings from 42 patients, acquired with a pneumatic system.

Training of head and neck segmentation networks with shape prior on small datasets.

Purpose: Cancer in the head and neck area is commonly treated with radiotherapy. A key step for low-risk treatment is the accurate delineation of organs at risk in the planning imagery. The success of deep learning in image segmentation led to automated algorithms achieving human expert performance on certain datasets.

Ejection Wave Segmentation for Contact-Free Heart Rate Estimation from Ballistocardiographic Signals.

We present a new algorithm for peak detection in ballistocardiographic (BCG) signals and use it for heart rate estimation. Systolic complexes of the BCG signal are enhanced and coarse heart beat locations estimated. Ejection waves I, J and K are detected simultaneously around coarse locations, only using detection of local maxima and weighted summation of peak heights.

Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach.

Purpose: In radiation therapy, a key step for a successful cancer treatment is image-based treatment planning. One objective of the planning phase is the fast and accurate segmentation of organs at risk and target structures from medical images. However, manual delineation of organs, which is still the gold standard in many clinical environments, is time-consuming and prone to inter-observer variations.

Model-Based Vestibular Afferent Stimulation: Evaluating Selective Electrode Locations and Stimulation Waveform Shapes.

A dysfunctional vestibular system can be a severe detriment to the quality of life of a patient. Recent studies have shown the feasibility for a vestibular implant to restore rotational sensation via electrical stimulation of vestibular ampullary nerves. However, the optimal stimulation site for selective elicitation of the desired nerve is still unknown.

Multi atlas based segmentation: should we prefer the best atlas group over the group of best atlases?

Multi atlas based segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases.

Analysis of Vestibular Labyrinthine Geometry and Variation in the Human Temporal Bone.

Stable posture and body movement in humans is dictated by the precise functioning of the ampulla organs in the semi-circular canals. Statistical analysis of the interrelationship between bony and membranous compartments within the semi-circular canals is dependent on the visualization of soft tissue structures. Thirty-one human inner ears were prepared, post-fixed with osmium tetroxide and decalcified for soft tissue contrast enhancement.

Model-based Vestibular Afferent Stimulation: Modular Workflow for Analyzing Stimulation Scenarios in Patient Specific and Statistical Vestibular Anatomy.

Our sense of balance and spatial orientation strongly depends on the correct functionality of our vestibular system. Vestibular dysfunction can lead to blurred vision and impaired balance and spatial orientation, causing a significant decrease in quality of life. Recent studies have shown that vestibular implants offer a possible treatment for patients with vestibular dysfunction.

Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

Purpose: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms.

Nine-degrees-of-freedom flexmap for a cone-beam computed tomography imaging device with independently movable source and detector.

Purpose: Couch-mounted cone-beam computed tomography (CBCT) imaging devices with independently rotatable x-ray source and flat-panel detector arms for acquisitions of arbitrary regions of interest (ROI) have recently been introduced in image-guided radiotherapy (IGRT). This work analyzes mechanical limitations and gravity-induced effects influencing the geometric accuracy of images acquired with arbitrary angular constellations of source and detector in nonisocentric trajectories, which is considered essential for IGRT. In order to compensate for geometric inaccuracies of this modality, a 9-degrees-of-freedom (9-DOF) flexmap correction approach is presented, focusing especially on the separability of the flexmap parameters of the independently movable components of the device.

Technical Note: plastimatch mabs, an open source tool for automatic image segmentation.

Purpose: Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing.

Contour-Driven Atlas-Based Segmentation.

We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior.

Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours.

Purpose: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low.

Vision 20/20: perspectives on automated image segmentation for radiotherapy.

Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition.

Interactive medical image segmentation using PDE control of active contours.

Segmentation of injured or unusual anatomic structures in medical imagery is a problem that has continued to elude fully automated solutions. In this paper, the goal of easy-to-use and consistent interactive segmentation is transformed into a control synthesis problem. A nominal level set partial differential equation (PDE) is assumed to be given; this open-loop system achieves correct segmentation under ideal conditions, but does not agree with a human expert's ideal boundary for real image data.

Hip fracture discrimination from dual-energy X-ray absorptiometry by statistical model registration.

Although the areal Bone Mineral Density (BMD) measurements from dual-energy X-ray absorptiometry (DXA) are able to discriminate between hip fracture cases and controls, the femoral strength is largely determined by the 3D bone structure. In a previous work a statistical model was presented which parameterizes the 3D shape and BMD distribution of the proximal femur. In this study the parameter values resulting from the registration of the model onto DXA images are evaluated for their hip fracture discrimination ability with respect to regular DXA derived areal BMD measurements.

3-D graph cut segmentation with Riemannian metrics to avoid the shrinking problem.

Though graph cut based segmentation is a widely-used technique, it is known that segmentation of a thin, elongated structure is challenging due to the "shrinking problem". On the other hand, many segmentation targets in medical image analysis have such thin structures. Therefore, the conventional graph cut method is not suitable to be applied to them.

A statistical model of shape and bone mineral density distribution of the proximal femur for fracture risk assessment.

This work presents a statistical model of both the shape and Bone Mineral Density (BMD) distribution of the proximal femur for fracture risk assessment. The shape and density model was built from a dataset of Quantitative Computed Tomography scans of fracture patients and a control group. Principal Component Analysis and Horn's parallel analysis were used to reduce the dimensionality of the shape and density model to the main modes of variation.

Texture analysis, bone mineral density, and cortical thickness of the proximal femur: fracture risk prediction.

Objective: The objectives of this study were to perform a clinical study analyzing bone quality in multidetector computed tomographic images of the femur using bone mineral density (BMD), cortical thickness, and texture algorithms in differentiating osteoporotic fracture and control subjects; to differentiate fracture types.

Methods: Femoral head, trochanteric, intertrochanteric, and upper and lower neck were segmented (fracture, n = 30; control, n = 10). Cortical thickness, BMD, and texture analysis were obtained using co-occurrence matrices, Minkowski dimension, and functional and scaling index method.

Assessment of the individual fracture risk of the proximal femur by using statistical appearance models.

Purpose: Standard diagnostic techniques to quantify bone mineral density (BMD) include dual-energy x-ray absorptiometry (DXA) and quantitative computed tomography. However, BMD alone is not sufficient to predict the fracture risk for an individual patient. Therefore, the development of tools, which can assess the bone quality in order to predict individual biomechanics of a bone, would mean a significant improvement for the prevention of fragility fractures.

Analysis of the micro-migration of sliding hip screws by using point-based registration.

Purpose: The favored treatment for many hip fractures is a sliding hip screw, and its usage is expected to increase in the future. Failures can be reduced, and complications detected earlier by semi-automated CT image analysis. The most frequent failure is due to the screw cut-out from the femoral head.

Statistical model based analysis of bone mineral density of lumbar spine.

Objective: For planning surgical interventions at the spine affected by osteoporosis, accurate information about the local bone quality in terms of anchorage strength for implants is very important. Based on previous work on automated bone quality assessment on the proximal femur with a completely automated model-based approach, this paper describes first applications and results on the lumbar vertebrae.

Materials And Methods: As basis for the analysis, CT datasets of 17 spinal specimens, with a resolution of 0.

Prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression.

Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose.

Trabecular bone analysis in CT and X-ray images of the proximal femur for the assessment of local bone quality.

Currently, conventional X-ray and CT images as well as invasive methods performed during the surgical intervention are used to judge the local quality of a fractured proximal femur. However, these approaches are either dependent on the surgeon's experience or cannot assist diagnostic and planning tasks preoperatively. Therefore, in this work a method for the individual analysis of local bone quality in the proximal femur based on model-based analysis of CT- and X-ray images of femur specimen will be proposed.