https://github.com/junma11/flare

Official Repository of MICCAI FLARE Challenges
https://github.com/junma11/flare

evaluation metrics segmentation

Last synced: 14 days ago
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Official Repository of MICCAI FLARE Challenges

• Host: GitHub
• URL: https://github.com/junma11/flare
• Owner: JunMa11
• License: mit
• Created: 2021-03-17T14:32:16.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-29T23:10:51.000Z (8 months ago)
• Last Synced: 2024-10-05T21:05:28.748Z (about 2 months ago)
• Topics: evaluation, metrics, segmentation
• Language: Python
• Homepage: https://www.codabench.org/competitions/2319/
• Size: 27 MB
• Stars: 60
• Watchers: 3
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Official Repository of FLARE Challenge

This repository provides the evaluation code of the FLARE Challenges. More details are available on the corresponding websites.

## [MICCAI FLARE 2021](https://flare.grand-challenge.org/FLARE21/): Fast and Low GPU memory Abdominal oRgan sEgmentation in CT Scans

- 511 cases from 11 medical centers

- Fully supervised setting

- Four segmentation targets: liver, kidney, spleen, and pancreas

- Evaluation metrics: DSC, NSD, Running time, Maximum GPU memory consumption

## [MICCAI FLARE 2022](https://flare22.grand-challenge.org/): Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation in CT Scans

- 2300 CT scans from 50+ medical centers

- Semi-supervised setting

- 13 segmentation targets:  liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum

- Evaluation metrics: DSC, NSD, Running time, Area under GPU memory-time curve and Area under CPU utilization-time curve

## [MICCAI FLARE 2023](https://codalab.lisn.upsaclay.fr/competitions/12239): Fast, Low-resource, and Accurate oRgan and Pan-cancer sEgmentation in Abdomen CT

- 4000 CT scans from 50+ medical centers

- Partial-label setting

- 14 segmentation targets:  liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, duodenum, and tumor

- Evaluation metrics: DSC, NSD, Running time, and Area under GPU memory-time curve

## MICCAI FLARE 2024: Fast, Low-resource, and Accurate oRgan and Pan-cancer sEgmentation

- [Task 1: Pan-cancer segmentation in CT scans](https://www.codabench.org/competitions/2319/)

- [Task 2: Abdominal CT Organ Segmentation on Laptop](https://www.codabench.org/competitions/2320/)

- [Task 3: Unsupervised Domain Adaptation for Abdominal Organ Segmentation in MRI Scans](https://www.codabench.org/competitions/2296/)