BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH CALSCALE:GREGORIAN PRODID:-//WordPress - MECv5.21.5//EN X-ORIGINAL-URL:https://cibm.ch/ BEGIN:VEVENT UID:MEC-8763d72bba4a7ade23f9ae1f09f4efc7@cibm.ch DTSTART:20211001T000000Z DTEND:20211002T000000Z DTSTAMP:20210521T000000Z CREATED:20210521 LAST-MODIFIED:20210617 SUMMARY:FeTA Challenge @MICCAI 2021 DESCRIPTION:Fetal Brain Tissue Annotation and Segmentation Challenge (FeTA), MICCAI 2021\nThe Fetal Brain Tissue Annotation and Segmentation Challenge (FeTA) is a multi-class image segmentation challenge organized as part of MICCAI 2021. The goal of FeTA is to compare automatic multi-class segmentation methods for the segmentation of developing human brain tissues. The challenge provides manually annotated, super-resolution reconstructed MRI data of human fetal brains which will be used for training and testing automated multi-class image segmentation algorithms.\nThe full challenge design can be found here: Fetal Brain Tissue Annotation and Segmentation Challenge | Zenodo\nData Release\nThe data for the FeTA Challenge has been released! Click here for details: FeTA Dataset\nImportant Dates\n\nData Release: May 3, 2021\n\nDocker Submission Deadline: July 26, 2021\nAlgorithm Description Deadline: August 5, 2021\nNotification of Presentations to top teams: End of August/Beginning of September\n\n\nWebsite of the event here\n\n X-ALT-DESC;FMTTYPE=text/html:
The Fetal Brain Tissue Annotation and Segmentation Challenge (FeTA) is a multi-class image segmentation challenge organized as part of MICCAI 2021. The goal of FeTA is to compare automatic multi-class segmentation methods for the segmentation of developing human brain tissues. The challenge provides manually annotated, super-resolution reconstructed MRI data of human fetal brains which will be used for training and testing automated multi-class image segmentation algorithms.
The full challenge design can be found here: Fetal Brain Tissue Annotation and Segmentation Challenge | Zenodo
The data for the FeTA Challenge has been released! Click here for details: FeTA Dataset