Accurate computing, modeling and analysis of the whole heart substructures is important in the development of clinical applications. Segmentation and registration of whole heart images is however challenging and still relies heavily on manual work, which is time-consuming and prone to errors.
The Challenge provides 120 multi-modality cardiac images acquired in real clinical environment. It aims at creating an open and fair competition for various research groups to test and validate their methods, particularly for the multi-modality whole heart segmentation. It is not only to benchmark various whole heart segmentation algorithms, but also to cover the topic of general cardiac image segmentation and registration and modeling.
You are welcomed to use the data or results for your publications. Please cite the references below when using them:
[1]
S Gao, H Zhou, Y Gao, X Zhuang. BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability. Medical Image Analysis 89, 102889, 2023
code&tutorial,
link
(Elsevier-MedIA 1st Prize & Best Paper Award of MICCAl society 2023)
[2]
Xiahai Zhuang: Multivariate mixture model for myocardial segmentation combining multi-source images.
IEEE Transactions on Pattern Analysis and Machine Intelligence 41(12): 2933-2946, 2019.
link code
[3] X Luo & X Zhuang: X-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(7): 9206 - 9224, 2023 (IF: 24.314)
link
code
[4] F Wu & X Zhuang.
Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T PAMI) 45(5): 6021 - 6036, 2023
link
code
The purpose of disseminating the Data is to perform a multi-institutional analysis of a database of anonymized clinical MRI and CT scans for whole heart segmentation. This analysis is taking place in the context of the Multi-Modality Whole Heart Segmentation Challenge 2017. The Recipient(s) commit to not disseminate the Data to any third party. For details of the data, please refer to here.
For participants who want to download and use the data, they need to agree with the conditions above and the terms in the registration form (please sign the form and send to the organizers.)
Agree and download from mega link.
* Users can evaluate their results by themselves now,
link
* Tools for MM-atlas WHS:
Atlas-based WHS (matlab: zxhwhs.m) ;
Multi-atlas WHS (zxhLabelFuse for label fusion) ;
ZXHPROJ ;
☆ Twenty MR images with manual segmentation, a sub set of MM-WHS challenge test set.
☆ Left Atrial and Scar Quantification & Segmentation Challenge 2022, WHS (TODO), (challenge link).
☆ Left Atrium Segmentation Challenge 13: WHS of 30 MR and 30 CT (challenge link).
☆ Coronary Centerline Extraction Challenge 08: WHS of 32 CTA, (original challenge-invalid now, challenge paper).
☆ Coronary Artery Stenosis Detection Challenge 13: WHS of 48 CTA, (original challenge-invalid now, challenge paper).
★ Medical image analysis (DATA630015) course data: pre-processed training data registered to a common space (2x2x2 mm), e.g. for multi-atlas segmentation, combined multi-modal image registration and segmentation, or statistical shape model studies: affregcommon2mm_roi_mr_train or affregcommon2mm_roi_ct_train.
★ All cardiac cross-modality domain adaptation works from zmic link
(1) Wu,FP's medical cross modality domain adaptation work & Data link1 link2-2DWHS
(2) Dou, Q et al.'s medical cross modality domain adaptation work link
