Quantifiable brain atrophy synthesis for benchmarking of cortical thickness estimation methods.

Cortical thickness (CTh) is routinely used to quantify grey matter atrophy as it is a significant biomarker in studying neurodegenerative and neurological conditions. Clinical studies commonly employ one of several available CTh estimation software tools to estimate CTh from brain MRI scans. In recent years, machine learning-based methods emerged as a faster alternative to the main-stream CTh estimation methods (e.

Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study.

Background: Proximal femoral fractures are an important clinical and public health issue associated with substantial morbidity and early mortality. Artificial intelligence might offer improved diagnostic accuracy for these fractures, but typical approaches to testing of artificial intelligence models can underestimate the risks of artificial intelligence-based diagnostic systems.

Methods: We present a preclinical evaluation of a deep learning model intended to detect proximal femoral fractures in frontal x-ray films in emergency department patients, trained on films from the Royal Adelaide Hospital (Adelaide, SA, Australia).

Towards data-driven quantification of skin ageing using reflectance confocal microscopy.

Introduction: Evaluation of skin ageing is a non-standardized, subjective process, with typical measures relying coarse, qualitatively defined features. Reflectance confocal microscopy depth stacks contain indicators of both chrono-ageing and photo-ageing. We hypothesize that an ageing scale could be constructed using machine learning and image analysis, creating a data-driven quantification of skin ageing without human assessment.

Investigation of the inherent left-right flow balancing of rotary total artificial hearts by means of a resistance box.

With the incidence of end-stage heart failure steadily increasing, the need for a practical total artificial heart (TAH) has never been greater. Continuous flow TAHs (CFTAH) are being developed using rotary blood pumps (RBPs), leveraging their small size, mechanical simplicity, and excellent durability. To completely replace the heart with currently available RBPs, two are required; one for providing pulmonary flow and one for providing systemic flow.

Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI.

We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation).

A Starling-like total work controller for rotary blood pumps: An in vitro evaluation.

Due to improved durability and survival rates, rotary blood pumps (RBPs) are the preferred left ventricular assist device when compared to volume displacement pumps. However, when operated at constant speed, RBPs lack a volume balancing mechanism which may result in left ventricular suction and suboptimal ventricular unloading. Starling-like controllers have previously been developed to balance circulatory volumes; however, they do not consider ventricular workload as a feedback and may have limited sensitivity to adjust RBP workload when ventricular function deteriorates or improves.

Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care.

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

Why rankings of biomedical image analysis competitions should be interpreted with care.

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now.

Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.

We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodology lies in the use of deep learning models for the problem of jointly classifying unregistered mammogram views and respective segmentation maps of breast lesions (i.e.

Identifying relevant biomarkers of brain injury from structural MRI: Validation using automated approaches in children with unilateral cerebral palsy.

Previous studies have proposed that the early elucidation of brain injury from structural Magnetic Resonance Images (sMRI) is critical for the clinical assessment of children with cerebral palsy (CP). Although distinct aetiologies, including cortical maldevelopments, white and grey matter lesions and ventricular enlargement, have been categorised, these injuries are commonly only assessed in a qualitative fashion. As a result, sMRI remains relatively underexploited for clinical assessments, despite its widespread use.

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease.

Closed-form equation to estimate the dielectric properties of biological tissues as a function of age.

Developing microwave systems for biomedical applications requires accurate dielectric properties of biological tissues for reliable modeling before prototyping and subject testing. Dielectric properties of tissues decrease with age due to the change in their water content, but there are no detailed age-dependent data, especially for young tissue-like newborns, in the literature. In this article, an age-dependent formula to predict the dielectric properties of biological tissues was derived.

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification.

Dielectric properties of dog brain tissue measured in vitro across the 0.3-3 GHz band.

Dielectric properties of dead Greyhound female dogs' brain tissues at different ages were measured at room temperature across the frequency range of 0.3-3 GHz. Measurements were made on excised tissues, in vitro in the laboratory, to carry out dielectric tests on sample tissues.

Automated, quantitative measures of grey and white matter lesion burden correlates with motor and cognitive function in children with unilateral cerebral palsy.

White and grey matter lesions are the most prevalent type of injury observable in the Magnetic Resonance Images (MRIs) of children with cerebral palsy (CP). Previous studies investigating the impact of lesions in children with CP have been qualitative, limited by the lack of automated segmentation approaches in this setting. As a result, the quantitative relationship between lesion burden has yet to be established.

Alterations in regional shape on ipsilateral and contralateral cortex contrast in children with unilateral cerebral palsy and are predictive of multiple outcomes.

Congenital brain lesions result in a wide range of cerebral tissue alterations observed in children with cerebral palsy (CP) that are associated with a range of functional impairments. The relationship between injury severity and functional outcomes, however, remains poorly understood. This research investigates the differences in cortical shape between children with congenital brain lesions and typically developing children (TDC) and investigates the correlations between cortical shape and functional outcome in a large cohort of patients diagnosed with unilateral CP.

Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy.

Understanding the relationships between the structure and function of the brain largely relies on the qualitative assessment of Magnetic Resonance Images (MRIs) by expert clinicians. Automated analysis systems can support these assessments by providing quantitative measures of brain injury. However, the assessment of deep gray matter structures, which are critical to motor and executive function, remains difficult as a result of large anatomical injuries commonly observed in children with Cerebral Palsy (CP).

A Parametric Simulation of Neuronal Noise From Microelectrode Recordings.

In this paper we present an efficient model of microelectrode recordings (MER) from the subthalamic nucleus acquired during deep brain stimulation (DBS) surgery. The model shows how changes in the "noise" relate to the neuronal spike time statistics. A top-down approach is used with analysis-by-synthesis of the MER power spectra.

A two-stage method to correct aberrations induced by slide slant in bright-field microscopy.

To achieve optimal image quality in bright field microscopy, the slide surface should be perpendicular to the optical axis of the microscope. However, in the recently proposed "slanted scan" slide acquisition technique, scan speed is increased by purposely slanting the slide by a small angle (of 3-5°) so that multiple focal depths can be imaged simultaneously. In this case, the slanted slide introduces a bend in the point spread function (PSF), resulting in a coma and other aberrations that degrade image quality.

Automated Segmentation of Skin Strata in Reflectance Confocal Microscopy Depth Stacks.

Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section.

Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells.

In this paper, we introduce and evaluate the systems submitted to the first Overlapping Cervical Cytology Image Segmentation Challenge, held in conjunction with the IEEE International Symposium on Biomedical Imaging 2014. This challenge was organized to encourage the development and benchmarking of techniques capable of segmenting individual cells from overlapping cellular clumps in cervical cytology images, which is a prerequisite for the development of the next generation of computer-aided diagnosis systems for cervical cancer. In particular, these automated systems must detect and accurately segment both the nucleus and cytoplasm of each cell, even when they are clumped together and, hence, partially occluded.

Temporal associations between arousal and body/limb movement in children with suspected obstructed sleep apnoea.

The inter-relationship between arousal events and body and/or limb movements during sleep may significantly impact the performance and clinical interpretation of actigraphy. As such, the objective of this study was to quantify the temporal association between arousals and body/limb movement. From this, we aim to determine whether actigraphy can predict arousal events in children, and identify the impact of arousal-related movements on estimates of sleep/wake periods.

A method for quantitative analysis of clump thickness in cervical cytology slides.

Knowledge of the spatial distribution and thickness of cytology specimens is critical to the development of digital slide acquisition techniques that minimise both scan times and image file size. In this paper, we evaluate a novel method to achieve this goal utilising an exhaustive high-resolution scan, an over-complete wavelet transform across multi-focal planes and a clump segmentation of all cellular materials on the slide. The method is demonstrated with a quantitative analysis of ten normal, but difficult to scan Pap stained, Thin-prep, cervical cytology slides.

An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells.

In this paper, we present an improved algorithm for the segmentation of cytoplasm and nuclei from clumps of overlapping cervical cells. This problem is notoriously difficult because of the degree of overlap among cells, the poor contrast of cell cytoplasm and the presence of mucus, blood, and inflammatory cells. Our methodology addresses these issues by utilizing a joint optimization of multiple level set functions, where each function represents a cell within a clump, that have both unary (intracell) and pairwise (intercell) constraints.