Medical Imaging

For the development and evaluation of organ localization methods, we build a set of annotations of organ bounding boxes based on the MICCAI Liver Tumor Segmentation (LiTS) challenge dataset. Bounding boxes of 11 body organs are included:  heart (53/28), left lung (52/21), right lung (52/21), liver (131/70), spleen (131/70), pancreas (131/70), left kidney (129/70), right kidney (131/69), bladder (109/67), left femoral head (109/66) and right femoral head (105/66). The number in the parentheses indicates the number of the organs annotated in training and testing sets.

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  • Biomedical and Health Sciences
  • Last Updated On: 
    Mon, 04/15/2019 - 03:09

    We make our dataset publicly avaiable. It consists of 50 H&E stained histopathology annotated images at the nuclei level. This dataset is ideal for those who want an exhaustive annotation of H&E breast cancer patient from a Tripple Negative Breast Cancer cohort.

    936 views
  • Medical Imaging
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    Access the dataset for images of typical diabetic retinopathy lesions and also normal retinal structures annotated at a pixel level, focused on an Indian population. This dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image.

    29478 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Tue, 11/12/2019 - 10:38

    Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these type of noise artifacts for removal in experimental photoacoustic data.

    843 views
  • Medical Imaging
  • Last Updated On: 
    Thu, 08/01/2019 - 21:49

    These datasets were used to produce the results of the following TMI paper: "3D Quantification of Filopodia in Motile Cancer Cells", Castilla C., et al. (2019). IEEE Transactions on Medical Imaging 38(3):862,872.

     

    160 views
  • Medical Imaging
  • Last Updated On: 
    Wed, 01/08/2020 - 07:21

    The investigation of the performance of different Positron Emission Tomography (PET) reconstruction and motion compensation methods requires an accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well- controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes and physiological variations. Alternatively, computational phantoms can be used to generate large datasets for different disease states, providing a ground truth.

    110 views
  • Medical Imaging
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    A database of lips traces
    Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.

    640 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Sat, 06/16/2018 - 23:18

    The class of registration methods proposed in the framework of Stokes Large Deformation
    Diffeomorphic Metric Mapping is a particularly interesting family of physically
    meaningful diffeomorphic registration methods.
    Stokes-LDDMM methods are formulated as a conditioned variational problem,
    where the different physical models are imposed using the associated partial differential equations
    as hard constraints.
    The most significant limitation of Stokes-LDDMM framework is its huge computational complexity.

    62 views
  • Medical Imaging
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

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