Supplementary Information for the Fetal Tissue Annotation 2022 Challenge Results

The Fetal Tissue Annotation and Segmentation Challenge (FeTA) is a multi-class, multi-institution image segmentation challenge part of MICCAI 2022. The goal of FeTA is to develop generalizable automatic multi-class segmentation methods for the segmentation of developing human brain tissues that will work with data acquired at different hospitals. This document is the supplementary information corresponding to the FeTA 2022 Challenge Results paper (https://arxiv.org/abs/2402.09463).

This document contains the methods descriptions of each submission to FeTA 2022, as well as the detailed ranking reports for the challenge results.

The teams that participated in the FeTA 2022 Challenge are: 

ajoshiusc
Blackbean
BlueBrune 
deepsynth
Dolphins: Coarse-to-Fine Models for FeTA2022 Segmentation 
FeTA-Imperial-TUM Team (FIT_1) – FIT-nnU-Net
FeTA-Imperial-TUM Team (FIT_2) – FIT-SwinUNETR
FMRSK 
fudan_zmic
hilab 
Neurophet
NVAUTO
Pasteur DBC
Sano
symsense
UNIANDES
xinlab-scut-iai-ahu0

This work was supported by the URPP Adaptive Brain Circuits in Development and Learning (AdaBD) project, the Vontobel Foundation, the Anna Müller Grocholski Foundation, the EMDO Foundation and the Prof. Dr Max Cloetta Foundation, the Swiss National Science Foundation (SNSF 320030_184932, 205321–182602), the Austrian Science Fund FWF [P 35189-B, I 3925-B27] and Vienna Science and Technology Fund WWTF [LS20-030]. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole polytechnique fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG). This work was supported by the NIH (Human Placenta Project—grant 1U01HD087202‐01), Wellcome Trust Sir Henry Wellcome Fellowship (201374/Z/16/Z and /B), UKRI FLF (MR/T018119/1), EPSRC (EP/V034537/1), and by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z].