Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System.

The reproduction of the brain 'sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin-Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.

Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification.

The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable diagnostic instruments to help medical personnel. Deep learning (DL) has been demonstrated to be useful for diagnosis using both computed tomography (CT) scans and chest X-rays (CXR), whereby the former typically yields more accurate results. However, the pivoting function of a CT scan during the pandemic presents several drawbacks, including high cost and cross-contamination problems. Radiation-free lung ultrasound (LUS) imaging, which requires high expertise and is thus being underutilised, has demonstrated a strong correlation with CT scan results and a high reliability in pneumonia detection even in the early stages. In this study, we developed a system based on modern DL methodologies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. Using a reliable dataset comprising ultrasound clips originating from linear and convex probes in 2908 frames from 450 hospitalised patients, we conducted an investigation into detecting Covid-19 patterns and ranking them considering two severity scales. This study differs from other research projects by its novel approach involving four and seven classes. Patients admitted to the ED underwent 12 LUS examinations in different chest parts, each evaluated according to standardised severity scales. We adopted residual convolutional neural networks (CNNs), transfer learning, and data augmentation techniques. Hence, employing methodological hyperparameter tuning, we produced state-of-the-art results meeting F1 score levels, averaged over the number of classes considered, exceeding 98%, and thereby manifesting stable measurements over precision and recall.

Granular layEr Simulator: Design and Multi-GPU Simulation of the Cerebellar Granular Layer.

In modern computational modeling, neuroscientists need to reproduce long-lasting activity of large-scale networks, where neurons are described by highly complex mathematical models. These aspects strongly increase the computational load of the simulations, which can be efficiently performed by exploiting parallel systems to reduce the processing times. Graphics Processing Unit (GPU) devices meet this need providing on desktop High Performance Computing. In this work, authors describe a novel Granular layEr Simulator development implemented on a multi-GPU system capable of reconstructing the cerebellar granular layer in a 3D space and reproducing its neuronal activity. The reconstruction is characterized by a high level of novelty and realism considering axonal/dendritic field geometries, oriented in the 3D space, and following convergence/divergence rates provided in literature. Neurons are modeled using Hodgkin and Huxley representations. The network is validated by reproducing typical behaviors which are well-documented in the literature, such as the center-surround organization. The reconstruction of a network, whose volume is 600 × 150 × 1,200 µm3 with 432,000 granules, 972 Golgi cells, 32,399 glomeruli, and 4,051 mossy fibers, takes 235 s on an Intel i9 processor. The 10 s activity reproduction takes only 4.34 and 3.37 h exploiting a single and multi-GPU desktop system (with one or two NVIDIA RTX 2080 GPU, respectively). Moreover, the code takes only 3.52 and 2.44 h if run on one or two NVIDIA V100 GPU, respectively. The relevant speedups reached (up to ~38× in the single-GPU version, and ~55× in the multi-GPU) clearly demonstrate that the GPU technology is highly suitable for realistic large network simulations.

Diabetic macular edema with neuroretinal detachment: OCT and OCT-angiography biomarkers of treatment response to anti-VEGF and steroids.

PURPOSE: To assess and compare early changes in neuroinflammatory and vascular parameters in diabetic macular edema (DME) with subfoveal neuroretinal detachment (SND) after treatment with intravitreal dexamethasone (DEX-I) and ranibizumab (IVR). METHODS: Thirty-three eyes (33 patients) with treatment naïve DME with SND were retrospectively evaluated at baseline and 2 months after DEX-I (15 eyes) and 1 month after 3 monthly IVR injections (18 eyes). Inclusion criteria were: complete eye examination, good quality OCT and OCT-A images. OCT parameters included: central macular thickness (CMT); number of hyper-reflective retinal spots (HRS) in inner, outer (IR, OR) and full retina; choroidal thickness (CT), extent of disorganization of inner retinal layers (DRIL), outer retina integrity (OR). On OCT-A: foveal avascular zone (FAZ) parameters in the superficial capillary plexus (SCP); cysts area and perfusion density (PD) in SCP and deep capillary plexus (DCP) and flow voids (FV) in choriocapillaris. FAZ was analyzed using ImageJ, perfusion parameters and FV using MATLAB. RESULTS: BCVA increased equally after both treatments (13.0 ± 10.0 ETDRS letters, p < 0.0001). There was a similar decrease (p < 0.05) in: height of SND, cysts area at SCP, central and mean CT, increase in FAZ perimeter and OR integrity, after both treatments. A greater decrease in DEX-I versus IVR group was found in: CMT (- 38.7% vs. - 22.2%, p = 0.012), HRS number in IR (- 29.2% vs. - 14.0%, p = 0.05) and full retina (- 24.7% vs. - 8.0%, p = 0.03), DRIL extension (- 62.0% vs. - 24%, p = 0.008), cysts area at DCP (- 68.7% vs. - 26.1%, p = 0.03), FAZ-CI (- 19.1% vs. - 8.3%, p = 0.02), PD at DCP (- 27.5% vs. + 4.9%, p = 0.02). FV did not change. CONCLUSIONS: More pronounced changes in specific inflammatory parameters in the inner retina are documented after steroid versus anti-VEGF treatment. These include reduction in HRS number, DRIL extension, CMT, cysts area at DCP. These data may help in further study of noninvasive imaging biomarkers for better evaluation of treatment response.

Quantitative choriocapillaris evaluation in intermediate age-related macular degeneration by swept-source optical coherence tomography angiography.

PURPOSE: To investigate choriocapillaris (CC) perfusion, by evaluating flow voids (FV), in eyes with intermediate age-related macular degeneration (iAMD) using swept-source optical coherence tomography angiography (SS-OCT-A). METHODS: Patients with bilateral or unilateral iAMD and normal controls underwent SS-OCT and OCT-A examination. Choriocapillaris (CC) FVs were quantitatively assessed on OCT-A images using matlab (version 2017b; MathWorks, Natick, MA, USA), after a preprocessing aimed at compensating for CC attenuation artefacts. Three different thresholds [1 standard deviation (SD), 1.25 SD and 1.5 SD] were applied. Final FV percentage (FV%) was calculated as the ratio between area with absent flow and total scanned area. RESULTS: Of 41 patients with iAMD and 16 normal subjects enrolled in the study, 39 eyes (39 patients) with iAMD and all 16 normal eyes (16 control subjects) were included in the final analysis. Mean FV% (1 SD) was 13.45 ± 0.66 in controls, 14.19 ± 1.23 in bilateral iAMD and 14.21 ± 0.99 in unilateral iAMD (p = 0.03, for difference between controls and bilateral iAMD). Mean FV% (1.25 SD) was 6.55 ± 0.65 in controls, 7.33 ± 1.4 in bilateral iAMD and 7.06 ± 1.4 in unilateral iAMD (p = 0.048, for difference between controls and bilateral iAMD). Mean FV% (1.5 SD) was 2.71 ± 0.82 in controls, 2.55 ± 1.12 in bilateral iAMD and 3.25 ± 1.17 in unilateral iAMD (p = 0.038, for difference between bilateral and unilateral iAMD). CONCLUSION: A significantly higher FV% was found in patients with iAMD versus controls. A higher trend in FV% was found in unilateral iAMD (with neovascular AMD in the fellow eye) versus bilateral iAMD, when applying the lowest threshold. Further, larger and longitudinal studies are needed to confirm this data.

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images.

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.