This data set contains:
- Training dataset: 271 CT-scans of inner ears used for optimization and training of the model.
- Validation dataset: 70 CT-scans of inner ears used for external validation.
- U-net architecture deep-learning model's weight after optimized training.
- All manual segmentations performed for both datasets.
- All post-processed automated segmentations performed by the model for bothd atasets.
The dataset used in the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos" is provided here. It contains both the segmentation and classification images with manual labels.
The 3DLSC-COVID datset includes a total of 1,805 3D chest CT scans with more than 570,000 CT slices were collected from 2 standard CT scanners of Liyuan Hospital, i.e., UIH uCT 510 and GE Optima CT600. Among all CT scans, there were 794 positive cases of COVID-19, which were further confirmed by clinical symptoms and RT-PCR from January 16 to April 16, 2020.
We chose 8 publicly available CT volumes of COVID-19 positive patients which were available from https://doi.org/10.5281/zenodo.3757476 and used 3D slicer to generate volumetric annotations of 512*512 dimension for 5 lung lobes namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe. These annotations are validated by a radiologist with over 15 years of experience.
This is the last in a series of challenges and competitons sponsored by IEEE Brain Initiative in 2017 that explore various brain/neuro datasets. Results and final presentations are expected to be made at the Boston (Cambridge) event, December 9, 2017. NOTE: EVENT IS STILL ON AS SCHEDULED DESPITE WEATHER.
COMPETITION DETAILS: https://brain.ieee.org/news/call-participation-ieee-brain-data-bank-challenge-boston/
REGISTRATION FORM: http://bit.ly/2xYX40o
