25,518 research outputs found
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Calcium imaging is an important technique for monitoring the activity of
thousands of neurons simultaneously. As calcium imaging datasets grow in size,
automated detection of individual neurons is becoming important. Here we apply
a supervised learning approach to this problem and show that convolutional
networks can achieve near-human accuracy and superhuman speed. Accuracy is
superior to the popular PCA/ICA method based on precision and recall relative
to ground truth annotation by a human expert. These results suggest that
convolutional networks are an efficient and flexible tool for the analysis of
large-scale calcium imaging data.Comment: 9 pages, 5 figures, 2 ancillary files; minor changes for camera-ready
version. appears in Advances in Neural Information Processing Systems 29
(NIPS 2016
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
We provide a comprehensive evaluation of salient object detection (SOD)
models. Our analysis identifies a serious design bias of existing SOD datasets
which assumes that each image contains at least one clearly outstanding salient
object in low clutter. The design bias has led to a saturated high performance
for state-of-the-art SOD models when evaluated on existing datasets. The
models, however, still perform far from being satisfactory when applied to
real-world daily scenes. Based on our analyses, we first identify 7 crucial
aspects that a comprehensive and balanced dataset should fulfill. Then, we
propose a new high quality dataset and update the previous saliency benchmark.
Specifically, our SOC (Salient Objects in Clutter) dataset, includes images
with salient and non-salient objects from daily object categories. Beyond
object category annotations, each salient image is accompanied by attributes
that reflect common challenges in real-world scenes. Finally, we report
attribute-based performance assessment on our dataset.Comment: ECCV 201
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The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.
Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community
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