Medical Imaging
The data is divided into a training set of 999 images and a test set of 335 images. The size of each 2D ultrasound image is 800 by 540 pixels with a pixel size ranging from 0.052 to 0.326 mm. The pixel size for each image can be found in the csv files: ‘training_set_pixel_size_and_HC.csv’ and ‘test_set_pixel_size.csv’. The training set also includes an image with the manual annotation of the head circumference for each HC, which was made by a trained sonographer.
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This dataset includes four sub-datasets: Drishti-GS, RIM-ONE-r3, ORIGA and REFUGE. Each image is cropped around the optic disc area for joint optic disc and cup segmentation. The size of all images is 512×512. The manual pixel-wise annotation is stored as a PNG image with the same size as the corresponding fundus image with the following labels:
128: Optic Disc (Grey color)
0: Optic Cup (Black color)
255: Background (White color)
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1026 Views
Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. One of the major stumbling blocks for manual retinal examination is the lack of a sufficient number of qualified medical personnel per capita to diagnose diseases.
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5576 Views
Abstract
This synthetic dataset or phantom consists of 6 raw format databases, in the three–dimensional (3-D) domain, which are identified as follows:
DB1: Ground Truth
DB2: Poisson noise
DB3: Stair-step artifact
DB4: Streak artifact
DB5: Both artifacts
DB6: Hybrid.
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732 Views
This repository contains the data related to the paper “CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging” (10.1109/TUFFC.2021.3131383). It contains multiple datasets used for training and testing, as well as the trained models and results (predictions and metrics). In particular, it contains a large-scale simulated training dataset composed of 31000 images for the three different imaging configuration considered (i.e., low quality, high quality, and ultrahigh quality).
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3115 Views
it contains the low-dose CT images used in the experiment.
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806 Views
Re-curated Breast Imaging Subset DDSM Dataset (RBIS-DDSM) is a curated version of 849 images from the CBIS-DDSM dataset available online with a permissive copyright license (CC-BY-SA 3.0). The CBIS-DDSM dataset is an improved version of the DDSM dataset. The authors of the CBIS-DDSM dataset attempted to improve the ground truth by applying simple image processing based methods to enhance the edges without any manual intervention from medical experts in order to segment and annotate masses. However, these annotations (segmentation maps) are inaccurate in most of the images.
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922 Views
Research data associated with paper: A Semantic Segmentation Model for Lumbar MRI Images using Divergence Loss, comprising the python code, a trained model and empirical results.
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365 Views