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.

Last Updated On: 
Sat, 02/27/2021 - 05:11

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.

Instructions: 

The file consists in a folder for each result whith the .obj and .nrrd files generated by the Nextmed algorithms.

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The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing.

Instructions: 

The datasets is made of a number of zip files. The name of the file identifies the figure (and figure panel) that the data refers to.

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Optical coherence tomography angiography (OCTA) is a novel imaging modality that allows a micron-level resolution to present the three-dimensional structure of the retinal vascular.

We propose a new multi-modality dataset, dubbed OCTA-500. It contains 500 subjects with 2 field of view (FOV) types, including OCT and OCTA volumes, 6 types of projections, 4 types of text labels and 2 types of pixel-level labels. This dataset contains more than 360K images with a size of about 80GB. 

Related Papers:

Instructions: 

An OCTA open access dataset for research only.

By using the OCTA-500 dataset, you are obliged to reference at least one of the following papers:

-Mingchao Li, Yerui Chen, Zexuan Ji, Keren Xie, Songtao Yuan, Qiang Chen, and Shuo Li.“Image projection network: 3D to 2D image segmentation in OCTA images,” IEEE Trans. Med. Imaging, vol. 39, no. 11 pp. 3343-3354, 2020.

-Mingchao Li, Yuhan Zhang, Zexuan Ji, Keren Xie, Songtao Yuan, Qinghuai Liu and Qiang Chen. "IPN-V2 and OCTA-500: Methodology and Dataset for Retinal Image Segmentation," arXiv:2012.07261.

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CHALLENGE ON ULTRASOUND BEAMFORMING WITH DEEP LEARNING (CUBDL)

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Some examples of the non-public data set ImageCLEF 2019 VQA-Med, including training, validation and test part.

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This dataset was used to investigate numerical methods of integration of the Frenet-Serret equations as applied to the study of vessel shape.  This data is a compliation of previously published data from the following papers:

A. V. Kamenskiy, J. N. MacTaggart, I. I. Pipinos, et al., “Three-dimensional geometry of the human carotid artery,” Journal of Biomechanical Engineering, vol. 134, no. 6, p. 064592, 2012.

Instructions: 

Data is organized by the publication from which it originated.  Each folder contains one or more csv files for either the individual vessel described in the file/folder name, or a collection of vessels corresponding to the patient imaged.  See original publications for further details regarding each dataset.  Within each .csv is, at a minimum, the x,y, and z vessel centerline coordinates, post processing.

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Features Extracted from BraTS 2012-2013

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An accurate analysis of fluid–structure interaction (FSI) at compliant arteries via ultrasound (US) imaging and numerical modeling is a limitation of several studies. In this study, we propose a deep learning-based boundary detection and compensation (DL-BDC) technique that can segment vessel boundaries by harnessing the convolutional neural network and wall motion compensation in near-wall flow dynamics. The segmentation performance of the technique is evaluated through numerical simulations with synthetic US images and in vitro experiments.

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