Background— The pathophysiology of aortic stenosis is incompletely understood, and the relative contributions of valvular calcification and inflammation to disease progression are unknown. Methods and Results— Patients with aortic sclerosis and mild, moderate, and severe stenosis were compared prospectively with age- and sex-matched control subjects. Aortic valve severity was determined by echocardiography. Calcification and inflammation in the aortic valve were assessed by 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG) uptake with the use of positron emission tomography. One hundred twenty-one subjects (20 controls; 20 aortic sclerosis; 25 mild, 33 moderate, and 23 severe aortic stenosis) were administered both 18F-NaF and 18F-FDG. Quantification of tracer uptake within the valve demonstrated excellent interobserver repeatability with no fixed or proportional biases and limits of agreement of ±0.21 (18F-NaF) and ±0.13 (18F-FDG) for maximum tissue-to-background ratios. Activity of both tracers was higher in patients with aortic stenosis than in control subjects (18F-NaF: 2.87±0.82 versus 1.55±0.17; 18F-FDG: 1.58±0.21 versus 1.30±0.13; both P <0.001). 18F-NaF uptake displayed a progressive rise with valve severity ( r 2 =0.540, P <0.001), with a more modest increase observed for 18F-FDG ( r 2 =0.218, P <0.001). Among patients with aortic stenosis, 91% had increased 18F-NaF uptake (>1.97), and 35% had increased 18F-FDG uptake (>1.63). A weak correlation between the activities of these tracers was observed ( r 2 =0.174, P <0.001). Conclusions— Positron emission tomography is a novel, feasible, and repeatable approach to the evaluation of valvular calcification and inflammation in patients with aortic stenosis. The frequency and magnitude of increased tracer activity correlate with disease severity and are strongest for 18F-NaF. Clinical Trial Registration— http://www.clinicaltrials.gov . Unique identifier: NCT01358513.

To demonstrate the diagnostic ability of label-free, point-scanning, fiber-based Fluorescence Lifetime Imaging (FLIm) as a means of intraoperative guidance during oral and oropharyngeal cancer removal surgery. Methods: FLIm point-measurements acquired from 53 patients (n = 67893 pre-resection in vivo, n = 89695 post-resection ex vivo) undergoing oral or oropharyngeal cancer removal surgery were used for analysis. Discrimination of healthy tissue and cancer was investigated using various FLIm-derived parameter sets and classifiers (Support Vector Machine, Random Forests, CNN). Classifier output for the acquired set of point-measurements was visualized through an interpolation-based approach to generate a probabilistic heatmap of cancer within the surgical field. Classifier output for dysplasia at the resection margins was also investigated. Results: Statistically significant change (P < 0.01) between healthy and cancer was observed in vivo for the acquired FLIm signal parameters (e.g., average lifetime) linked with metabolic activity. Superior classification was achieved at the tissue region level using the Random Forests method (ROC-AUC: 0.88). Classifier output for dysplasia (% probability of cancer) was observed to lie between that of cancer and healthy tissue, highlighting FLIm's ability to distinguish various conditions. Conclusion: The developed approach demonstrates the potential of FLIm for fast, reliable intraoperative margin assessment without the

In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function. Domain-specific normalisation and scaling operators are trained to allow the model to adjust to the statistical distributions of the various visual domains. The developed adaptation technique is used to produce a singular patch-based counting regressor capable of counting various object types including people, vehicles, cell nuclei and wildlife. As part of this study a challenging new cell counting dataset in the context of tissue culture and patient diagnosis is constructed. This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is the first of its kind to be made available to the wider computer vision community. State-of-the-art object counting performance is achieved in both the Shanghaitech (parts A and B) and Penguins datasets while competitive performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets, all using a shared counting model.

In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016). Producing an accurate and robust crowd count estimator using computer vision techniques has attracted significant research interest in recent years. Applications for crowd counting systems exist in many diverse areas including city planning, retail, and of course general public safety. Developing a highly generalised counting model that can be deployed in any surveillance scenario with any camera perspective is the key objective for research in this area. Techniques developed in the past have generally performed poorly in highly congested scenes with several thousands of people in frame (Rodriguez et al., 2011). Our approach, influenced by the work of (Zhang et al., 2016), consists of the following contributions: (1) A training set augmentation scheme that minimises redundancy among training samples to improve model generalisation and overall counting performance; (2) a deep, single column, fully convolutional network (FCN) architecture; (3) a multi-scale averaging step during inference. The developed technique can analyse images of any resolution or aspect ratio and achieves state-of-the-art counting performance on the Shanghaitech Part B and UCF CC 50 datasets as well as competitive performance on Shanghaitech Part A.