Supplementary Information for the Fetal Tissue Annotation 2022 Challenge Results
Creators: name: Payette, Kelly
name: Steger, Céline
name: Licandro, Roxane
name: de Dumast, Priscille
name: Li, Hongwei Bran
name: Barkovich, Matthew
name: Li, Liu
name: Dannecker, Maik
name: Chen, Chen
name: Ouyang, Cheng
name: McConnell, Niccolò
name: Miron, Alina
name: Li, Yongmin
name: Uus, Alena
name: Grigorescu, Irina
name: Ramirez Gilliland, Paula
name: Rahman Siddiquee, Md Mahfuzur
name: Xu, Daguang
name: Myronenko, Andriy
name: Wang, Haoyu
name: Huang, Ziyan
name: Ye, Jin
name: Alenyà, Mireia
name: Comte, Valentin
name: Camara, Oscar
name: Masson, Jean-Baptiste
name: Nilsson, Astrid
name: Godard, Charlotte
name: Mazher, Moona
name: Qayyum, Abdul
name: Gao, Yibo
name: Zhou, Hangqi
name: Gao, Shangqi
name: Fu, Jia
name: Dong, Guiming
name: Wang, Guotai
name: Rieu, ZunHyan
name: Yang, HyeonSik
name: Lee, Minwoo
name: Płotka, Szymon
name: Grzeszczyk, Michal K.
name: Sitek, Arkadiusz
name: Vargas Daza, Luisa
name: Usma, Santiago
name: Arbelaez, Pablo
name: Lu, Wenying
name: Zhang, Wenhao
name: Liang, Jing
name: Valabregue, Romain
name: Joshi, Anand A.
name: Nayak, Krishna N.
name: Leahy, Richard M.
name: Wilhelmi, Luca
name: Dändliker, Aline
name: Ji, Hui
name: Gennari, Antonio G.
name: Jakovčić, Anton
name: Klaić, Melita
name: Adžić, Ana
name: Marković, Pavel
name: Grabarić, Gracia
name: Kasprian, Gregor
name: Dovjak, Gregor
name: Rados, Milan
name: Vasung, Lana
name: Bach Cuadra, Meritxell
name: Jakab, Andras
Publisher: name: Zenodo
Year: 2024

description: 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: 

ajoshiuscBlackbeanBlueBrune deepsynthDolphins: Coarse-to-Fine Models for FeTA2022 Segmentation FeTA-Imperial-TUM Team (FIT_1) – FIT-nnU-NetFeTA-Imperial-TUM Team (FIT_2) – FIT-SwinUNETRFMRSK fudan_zmichilab NeurophetNVAUTOPasteur DBCSanosymsenseUNIANDESxinlab-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].