Vocal Folds Disorders Detection and Classification in Endoscopic Narrow-Band Images

The diagnosis of vocal folds (VF) diseases is error- prone due to the large variety of diseases that can affect them. VF lesions can be divided in nodular, e.g. nodules, polyps and cysts, and diffuse, e.g. hyperplastic laryngitis and carcinoma. By endoscopic examination, the clinician traditionally evaluates the presence of macroscopic formations and mucosal vessels alteration. Endoscopic narrow-band imaging (NBI) has recently started to be employed since it provides enhanced vessels contrast as compared to classical white-light endoscopy. This work presents a preliminary study on the development of an automatic diagnostic tool based on the assessment of vocal cords symmetry in NBI images. The objective is to identify possible protruding mass lesions on which subsequent vessels analysis may be performed. The method proposed here is based on the segmentation of the glottal area (GA) from the endoscopic images, based on which the right and the left portions of the vocal folds are detected and analyzed for the detection of protruding areas. The obtained information is then used to classify the VF edges as healthy or pathological. Results from the analysis of 22 endoscopic NBI images demonstrated that the proposed algorithm is robust and effective, providing a 100% success rate in the classification of VF edges as healthy or pathological. Such results support the investment in further research to expand and improve the algorithm presented here, potentially with the addition of vessels analysis to determine the pathological classification of detected protruding areas

DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio (SNR). Aim. The aim is to select a DTI sequence for brain clinical studies, optimizing SNR and resolution. Methods and Results. We applied 6 methods for SNR computation in 26 DTI sequences with different parameters using 4 healthy volunteers (HV). We choosed two DTI sequences for their high SNR, they differed by voxel size and b-value. Subsequently, the two selected sequences were acquired from 30 multiple sclerosis (MS) patients with different disability and lesion load and 18 age matched HV. We observed high concordance between mean diffusivity (MD) and fractional anysotropy (FA), nonetheless the DTI sequence with smaller voxel size displayed a better correlation with disease progression, despite a slightly lower SNR. The reliability of corpus callosum (CC) fiber tracking with the chosen DTI sequences was also tested. Conclusions. The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity

Brain-vascular segmentation for SEEG planning via a 3D fully-convolutional neural network

Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence. Moreover, a two-stage training has been proposed as a countermeasure for the unbalanced nature of the dataset. The FCNN training was performed on 20 CE-CBCT volumes exploiting mini-batch gradient descent andthe Adam optimizer. Binary cross-entropy was used as loss function. Performance evaluation was conducted considering 5 datasets. A median value of Dice, Precision and Recall of 0.79, 0.8 and 0.69 were obtained with respect to manual annotations

Beat-to-beat variability of microvascular peripheral resistances assessed with a non-invasive approach

The pressure-flow relationship at peripheral level is non-invasively studied in human subjects: the impedance function and the beat-to-beat variability series of microvascular peripheral resistance are estimated. The frequency content of this variability signal is compared to those of more classical variability series at rest and during mild supine physical exercise

Numerical algorithm to recover contrast dynamics in 3D digital subtraction angiography data-sets: a preliminary clinical validation

Several neurosurgical procedures, such as ArteroVenous Malformations (AVMs) and StereoElectroEncephaloGraphy (SEEG) require accurate reconstruction of the cerebral vascular tree, as well as the classification of arteries and veins, to increase the safety of the intervention. We propose ART-3.5D, a novel approach to recover the dynamic information from standard Cone Beam Computed Tomography Angiography scans based on the post- processing of both the segmented angiogram and the raw data-set

The TM6SF2 E167K genetic variant induces lipid biosynthesis and reduces apolipoprotein B secretion in human hepatic 3D spheroids

There is a high unmet need for developing treatments for nonalcoholic fatty liver disease (NAFLD), for which there are no approved drugs today. Here, we used a human in vitro disease model to understand mechanisms linked to genetic risk variants associated with NAFLD. The model is based on 3D spheroids from primary human hepatocytes from five different donors. Across these donors, we observed highly reproducible differences in the extent of steatosis induction, demonstrating that inter-donor variability is reflected in the in vitro model. Importantly, our data indicates that the genetic variant TM6SF2 E167K, previously associated with increased risk for NAFLD, induces increased hepatocyte fat content by reducing APOB particle secretion. Finally, differences in gene expression pathways involved in cholesterol, fatty acid and glucose metabolism between wild type and TM6SF2 E167K mutation carriers (N = 125) were confirmed in the in vitro model. Our data suggest that the 3D in vitro spheroids can be used to investigate the mechanisms underlying the association of human genetic variants associated with NAFLD. This model may also be suitable to discover new treatments against NAFLD

Rare Pathogenic Variants Predispose to Hepatocellular Carcinoma in Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a rising cause of hepatocellular carcinoma (HCC). We examined whether inherited pathogenic variants in candidate genes (n = 181) were enriched in patients with NAFLD-HCC. To this end, we resequenced peripheral blood DNA of 142 NAFLD-HCC, 59 NAFLD with advanced fibrosis, and 50 controls, and considered 404 healthy individuals from 1000 G. Pathogenic variants were defined according to ClinVar, likely pathogenic as rare variants predicted to alter protein activity. In NAFLD-HCC patients, we detected an enrichment in pathogenic (p = 0.024), and likely pathogenic variants (p = 1.9*10 126 ), particularly in APOB (p = 0.047). APOB variants were associated with lower circulating triglycerides and higher HDL cholesterol (p < 0.01). A genetic risk score predicted NAFLD-HCC (OR 4.96, 3.29\u20137.55; p = 5.1*10 1216 ), outperforming the diagnostic accuracy of common genetic risk variants, and of clinical risk factors (p < 0.05). In conclusion, rare pathogenic variants in genes involved in liver disease and cancer predisposition are associated with NAFLD-HCC development