Medical Imaging

Welcome to the Retinal Fundus Glaucoma Challenge! REFUGE was organized as a half day Challenge in conjunction with the 5th MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), a Satellite Event of the MICCAI 2018 conference in Granada, Spain. The goal of the challenge is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images. With this challenge, we made available a large dataset of 1200 annotated retinal fundus images.

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  • Computer Vision
  • Last Updated On: 
    Tue, 07/09/2019 - 08:57

    Pathologic Myopia Challenge (PALM), as a part of the serial challenge iChallenge, is organized as a half day Challenge, a Satellite Event of the ISBI 2019 conference in Venice, Italy. The PALM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients. The goal of the challenge is to evaluate and compare automated algorithms for the detection of pathological myopia on a common dataset of retinal fundus images.

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  • Computer Vision
  • Last Updated On: 
    Thu, 07/18/2019 - 23:43

    FRAP curve modeling using transient-sensitive analog computer unit with oscilloscopic CRT (Practicum, 2014)

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  • Neuroscience
  • Last Updated On: 
    Sat, 07/06/2019 - 07:39

    RECOVERY-FA19 dataset is established for development and evaulation of retinal vessel detection algorithms in fluorescein angiography (FA). RECOVERY-FA19 provides ultra-widefield FA images acquired using Optos California P200DTx camera and labeled binary vessel maps.

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  • Standards Research Data
  • Last Updated On: 
    Mon, 06/03/2019 - 15:22

    The provided EEG data were acquired from sixteen healthy young adults (age range 22 - 30 years) with no history of neurological, physical, or psychiatric illness. All the participants were naive BCI users who had not participated in any related experiments before. Informed consents were received from all participants.  The study has been approved by the Institutional Research Ethics Committee of Nazarbayev University.  

     

    716 views
  • Medical Imaging
  • Last Updated On: 
    Sun, 05/19/2019 - 08:11

    We present the first approach to automatically detect and track a needle in an endoscopic ultrasound (EUS) video. EUS is a fundamental medical procedure that is used extensively for biopsying targets in the upper gastro-intestinal tract. The approach enables various new applications for advancing EUS, including automatic EUS workflow analysis, automatic report generation, video summarization, technical skills assessment and quality indicator automation. Our approach does not require annotations of needles in videos, which is a laborious and demanding task.

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  • Medical Imaging
  • Last Updated On: 
    Fri, 03/29/2019 - 07:00

    The dataset contains 208 patient scans that spread over three parts of anatomy(head, neck, and pelvis). The dataset aims to establish accurate anatomical correspondences between MegaVoltage Planar Digital Radio-graphs (MV-DRs) andKiloVoltage Digital Reconstructed Radiographs (KV-DRRs), which are widely used in Image-Guided Radiation Therapy (IGRT) to verify patients’ positions for accurate radiotherapy delivery.

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  • Medical Imaging
  • Last Updated On: 
    Fri, 03/22/2019 - 10:43

    This study investigated possible long-term cardiotoxicity-related left-ventricular (LV) contractile dysfunction in breast cancer patients who had treatment with anti-neoplastic chemotherapy agents (CTA). An automated cardiac contractility analysis tool consisting of quantization-based boundary detection and meshfree Radial Point Interpolation Method-based numerical analysis measured torsion and 3D strains for comparisons to healthy subjects to investigate LV remodeling otherwise not indicated by LV ejection fraction (LVEF).

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  • Medical Imaging
  • Last Updated On: 
    Mon, 03/11/2019 - 01:26

    Dataset for journal manuscript titled: 'Cardiac Motion Estimation from Noisy Medical Images: A Regularisation Framework Applied on Pairwise Image Registration Displacement Fields'

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  • Medical Imaging
  • Last Updated On: 
    Wed, 09/11/2019 - 04:34

    The DualModal2019 is a dual-modal fundus image dataset. It is for vessel, arteriole, and venule segmentation tasks. The dataset consists of five types of images: RGB color images, the 570 nm and 610 nm monochromic images, and the corresponding manually annotated ground truth images of the arterioles and venules.

    384 views
  • Medical Imaging
  • Last Updated On: 
    Fri, 02/22/2019 - 01:07

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