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ShareScore release 0.9.0
Dataset results
5 results for “Fully convolutional network”
Data from: A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks
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Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
<p>We recorded the measurement data used in the present study on a single-span steel trough railway bridge located on a long-distance traffic line in Germany. The bridge is 18.4 m long in total with a free span of 16.4 m. A total of 10 seismic uniaxial accelerometers of the type PCB-39B04 (PCB Synotech) with a sensitivity of 1000 mV/g (±10\%), a broadband resolution of 0.000003 gRMS, a measurement range of ±5 gpk and a frequency range of 0.06 to 450 Hz (±5\%) were installed. The measurements are triggered via the rising slope of the wheel load measuring point G1, the measurements from the ring buffer are stored from ten seconds before the trigger together with the 50 seconds long measurement after the triggering. The recorded signals thus all have a length of 60 seconds. All sensor signals were recorded with a sampling frequency of fs = 600 Hz using the catmanAP software and the CX22 data recorder connected to an MX1601B universal amplifier and an MX1616B strain gauge amplifier (all products are from HBK). </p>
Traning set for "Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks"
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Dataset related to the article " Automated Left and Right Ventricular Chamber Segmentation in Cardiac Magnetic Resonance Images Using Dense Fully Convolutional Neural Network"
<p>This record contains raw data related to the article " Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network"</p> <p><br> Background and objective: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiacfunction evaluation and diagnosis. In order to have a common reference for comparing segmentation algorithms, several CMR image datasets were made available, but in general they do not include the most apical and basal slices, and/or gold standard tracing is limited to only one of the two ventricles, thus not fully corresponding to real clinical practice. Our aim was to develop a deep learning (DL) approach for automated segmentation of both RV and LV chambers from short-axis (SAX) CMR images, reporting separately the performance for basal slices, together with the applied criterion of choice.<br> Method: A retrospectively selected database (DB1) of 210 cine sequences (3 pathology groups) was considered: images (GE, 1.5 T) were acquired at Centro Cardiologico Monzino (Milan, Italy), and end-diastolic (ED) and end-systolic frames (ES) were manually segmented (gold standard, GS). Automatic ED and ES RV and LV segmentation were performed with a U-Net inspired architecture, where skip connections were redesigned introducing dense blocks to alleviate the semantic gap between the U-Net encoder and decoder. The proposed architecture was trained including: A) the basal slices where the Myo surrounded<br> the LV for at least the 50% and all the other slice; B) all the slices where the Myo completely surrounded the LV. To evaluate the clinical relevance of the proposed architecture in a practical use case scenario, a graphical user interface was developed to allow clinicians to revise, and correct when needed, the automatic segmentation. Additionally, to assess generalizability, analysis of CMR images obtained in 12 healthy volunteers (DB2) with different equipment (Siemens, 3T) and settings was performed.<br> Results: The proposed architecture outperformed the original U-Net. Comparing the performance on DB1 between the two criteria, no significant differences were measured when considering all slices together, but were present when only basal slices were examined. Automatic and manually-adjusted segmentation<br> performed similarly compared to the GS (bias±95%LoA): LVEDV -1±12 ml, LVESV -1±14 ml, RVEDV 6±12 ml, RVESV 6±14 ml, ED LV mass 6±26 g, ES LV mass 5±26 g). Also, generalizability showed very similar performance, with Dice scores of 0.944 (LV), 0.908 (RV) and 0.852 (Myo) on DB1, and 0.940 (LV), 0.880 (RV), and 0.856 (Myo) on DB2.<br> Conclusions: Our results support the potential of DL methods for accurate LV and RV contours segmentation and the advantages of dense skip connections in alleviating the semantic gap generated when high level features are concatenated with lower level feature. The evaluation on our dataset, considering separately the performance on basal and apical slices, reveals the potential of DL approaches for fast, accurate and reliable automated cardiac segmentation in a real clinical setting.<br> </p>
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Network
<p>Dataset related to the paper:<br> "LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks'' under review in Robotics and Autonomous Systems 2018 </p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.