Medical Imaging

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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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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1540 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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Akshi IMAGE, a new Indian ethnicity retinal fundus image database, has been established for the evaluation of computer-assisted glaucoma prescreening methods. ‘Akshi’ is a Sanskrit word for the ‘Eye’ and ‘IMAGE’ is an acronym for IISc-MAHE Glaucoma Evaluation database. This database is a result of an interdisciplinary collaboration between Indian Institute of Science (IISc) and Manipal Academy of Higher Education (MAHE). The database consists of retinal color fundus images acquired using three different devices.

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993 Views

 

The results of the experiemental work presented in the journal paper with the title : Reconstruction and Visualization of 5µm Sectional Coronal Views for Macula Vasculature in OCTA, which has the following abstratct

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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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 it contains the low-dose CT images used in the experiment.

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365 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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484 Views

Parasitic infections have been recognised as one of the most significant causes of illnesses by WHO. Most infected persons shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals. Diagnosis of intestinal parasites is usually based on direct examination in the laboratory, of which capacity is obviously limited.

Last Updated On: 
Mon, 09/05/2022 - 00:43
Citation Author(s): 
Duangdao Palasuwan, Thanarat H. Chalidabhongse, Korranat Naruenatthanaset, Thananop Kobchaisawat, Kanyarat Boonpeng, Nuntiporn Nunthanasup, Nantheera Anantrasirichai

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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