Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.

Diagnostic Accuracy of Copeptin in the Differential Diagnosis of the Polyuria-polydipsia Syndrome: A Prospective Multicenter Study

CONTEXT The polyuria-polydipsia syndrome comprises primary polydipsia (PP) and central and nephrogenic diabetes insipidus (DI). Correctly discriminating these entities is mandatory, given that inadequate treatment causes serious complications. The diagnostic "gold standard" is the water deprivation test with assessment of arginine vasopressin (AVP) activity. However, test interpretation and AVP measurement are challenging. OBJECTIVE The objective was to evaluate the accuracy of copeptin, a stable peptide stoichiometrically cosecreted with AVP, in the differential diagnosis of polyuria-polydipsia syndrome. DESIGN, SETTING, AND PATIENTS This was a prospective multicenter observational cohort study from four Swiss or German tertiary referral centers of adults >18 years old with the history of polyuria and polydipsia. MEASUREMENTS A standardized combined water deprivation/3% saline infusion test was performed and terminated when serum sodium exceeded 147 mmol/L. Circulating copeptin and AVP levels were measured regularly throughout the test. Final diagnosis was based on the water deprivation/saline infusion test results, clinical information, and the treatment response. RESULTS Fifty-five patients were enrolled (11 with complete central DI, 16 with partial central DI, 18 with PP, and 10 with nephrogenic DI). Without prior thirsting, a single baseline copeptin level >21.4 pmol/L differentiated nephrogenic DI from other etiologies with a 100% sensitivity and specificity, rendering a water deprivation testing unnecessary in such cases. A stimulated copeptin >4.9 pmol/L (at sodium levels >147 mmol/L) differentiated between patients with PP and patients with partial central DI with a 94.0% specificity and a 94.4% sensitivity. A stimulated AVP >1.8 pg/mL differentiated between the same categories with a 93.0% specificity and a 83.0% sensitivity. LIMITATION This study was limited by incorporation bias from including AVP levels as a diagnostic criterion. CONCLUSION Copeptin is a promising new tool in the differential diagnosis of the polyuria-polydipsia syndrome, and a valid surrogate marker for AVP. Primary Funding Sources: Swiss National Science Foundation, University of Basel.

Automatic assessment of time-resolved OCT images for selective retina therapy

Purpose In recent years, selective retina laser treatment (SRT), a sub-threshold therapy method, avoids widespread damage to all retinal layers by targeting only a few. While these methods facilitate faster healing, their lack of visual feedback during treatment represents a considerable shortcoming as induced lesions remain invisible with conventional imaging and make clinical use challenging. To overcome this, we present a new strategy to provide location-specific and contact-free automatic feedback of SRT laser applications. Methods We leverage time-resolved optical coherence tomography (OCT) to provide informative feedback to clinicians on outcomes of location-specific treatment. By coupling an OCT system to SRT treatment laser, we visualize structural changes in the retinal layers as they occur via time-resolved depth images. We then propose a novel strategy for automatic assessment of such time-resolved OCT images. To achieve this, we introduce novel image features for this task that when combined with standard machine learning classifiers yield excellent treatment outcome classification capabilities. Results Our approach was evaluated on both ex vivo porcine eyes and human patients in a clinical setting, yielding performances above 95 % accuracy for predicting patient treatment outcomes. In addition, we show that accurate outcomes for human patients can be estimated even when our method is trained using only ex vivo porcine data. Conclusion The proposed technique presents a much needed strategy toward noninvasive, safe, reliable, and repeatable SRT applications. These results are encouraging for the broader use of new treatment options for neovascularization-based retinal pathologies.

Scientific assessment of the quality of OSIRIS images

Context. OSIRIS, the scientific imaging system onboard the ESA Rosetta spacecraft, has been imaging the nucleus of comet 67P/Churyumov-Gerasimenko and its dust and gas environment since March 2014. The images serve different scientific goals, from morphology and composition studies of the nucleus surface, to the motion and trajectories of dust grains, the general structure of the dust coma, the morphology and intensity of jets, gas distribution, mass loss, and dust and gas production rates. Aims. We present the calibration of the raw images taken by OSIRIS and address the accuracy that we can expect in our scientific results based on the accuracy of the calibration steps that we have performed. Methods. We describe the pipeline that has been developed to automatically calibrate the OSIRIS images. Through a series of steps, radiometrically calibrated and distortion corrected images are produced and can be used for scientific studies. Calibration campaigns were run on the ground before launch and throughout the years in flight to determine the parameters that are used to calibrate the images and to verify their evolution with time. We describe how these parameters were determined and we address their accuracy. Results. We provide a guideline to the level of trust that can be put into the various studies performed with OSIRIS images, based on the accuracy of the image calibration.

Methods for assessment of keel bone damage in poultry

Keel bone damage (KBD) is a critical issue facing the laying hen industry today as a result of the likely pain leading to compromised welfare and the potential for reduced productivity. Recent reports suggest that damage, while highly variable and likely dependent on a host of factors, extends to all systems (including battery cages, furnished cages, and non-cage systems), genetic lines, and management styles. Despite the extent of the problem, the research community remains uncertain as to the causes and influencing factors of KBD. Although progress has been made investigating these factors, the overall effort is hindered by several issues related to the assessment of KBD, including quality and variation in the methods used between research groups. These issues prevent effective comparison of studies, as well as difficulties in identifying the presence of damage leading to poor accuracy and reliability. The current manuscript seeks to resolve these issues by offering precise definitions for types of KBD, reviewing methods for assessment, and providing recommendations that can improve the accuracy and reliability of those assessments.