Medical Imaging

This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.

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  • Biomedical and Health Sciences
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

    1141 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Tue, 11/12/2019 - 10:38

    BCI-Double-ErrP-Dataset is an EEG dataset recorded while participants used a P300-based BCI speller. This speller uses a P300 post-detection based on Error-related potentials (ErrPs) to detect and correct errors (i.e. when the detected symbol does not match the user’s intention). After the P300 detection, an automatic correction is made when an ErrP is detected (this is called a “Primary ErrP”). The correction proposed by the system is also evaluated, eventually eliciting a “Secondary ErrP” if the correction is wrong.

    83 views
  • Machine Learning
  • Last Updated On: 
    Fri, 03/20/2020 - 08:13

    BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’19 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.

  • Image Processing
  • Biomedical and Health Sciences
  • Medical Imaging
  • Last Updated On: 
    Fri, 02/28/2020 - 06:31

    This demo is intended to implement Ultrasound Localization Microscopy by a modified sub-pixel convolutional neural network (mSPCN-ULM). The detailed information can be referred in Liu et al. "Deep Learning for Ultrasound Localization Microscopy", which has been submitted to IEEE TRANSACTIONS ON MEDICAL IMAGING.

    25 views
  • Medical Imaging
  • Last Updated On: 
    Tue, 02/11/2020 - 08:05

    Summary:

    The transcranial Doppler echo data was recorded from both healthy and neurocritical care patients. The insonated cerebral vessels were the middle cerebral artery (MCA) and the internal carotid artery (ICA). The ultrasound device used in this study was the Philips CX50.

    124 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Wed, 02/19/2020 - 09:19

    ADAM is organized as a half day Challenge, a Satellite Event of the ISBI 2020 conference in Iowa City, Iowa, USA.

    82 views
  • Computer Vision
  • Last Updated On: 
    Mon, 01/20/2020 - 07:52

    It includes 312 ROIs. An ROI is a rectangular BMP image region. A rectangular image region  is located within a PDAC tumor region or within a HP region of a slice CT image. ROIs of 1-153 are PDAC, ROIs of 154:312 are HP.

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  • Artificial Intelligence
  • Last Updated On: 
    Mon, 01/20/2020 - 03:59

    Endoscopy is a widely used clinical procedure for the early detection of cancers in hollow-organs such as oesophagus, stomach, and colon. Computer-assisted methods for accurate and temporally consistent localisation and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos which is critical for monitoring and surgical planning. Innovations have the potential to improve current medical practices and refine healthcare systems worldwide.

  • Artificial Intelligence
  • Computer Vision
  • Image Processing
  • Machine Learning
  • Biomedical and Health Sciences
  • Medical Imaging
  • Last Updated On: 
    Tue, 02/11/2020 - 00:40

    Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and  a platform for the visualization of the results as 3D models.

    This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.

    This dataset relates to 718 of the 1012 LIDC-IDRI scans.

    152 views
  • Artificial Intelligence
  • Last Updated On: 
    Thu, 02/27/2020 - 10:07

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