Join QuantConn a MICCAI 2023 harmonization challenge for dMRI data!
Eleftherios Garyfallidis
Hello Imagers!,
This summer I am involved with the co-organization of a really exciting scientific challenge. Therefore, I am writing to introduce you to QuantConn, a MICCAI 2023 competition that aims to address the challenge of harmonizing diverse DW (Diffusion-Weighted) images acquired from two distinct diffusion acquisition protocols, both collected in the same scanning session.
The primary objective of this competition is to preprocess the DW images from the two acquisition protocols, making them as similar as possible while preserving key information. We encourage participants to explore a wide range of methods and techniques within the preprocessing pipeline to achieve this goal. Your innovative solutions could include explicit image harmonization methods, denoising approaches, super resolution techniques, or any other strategies that effectively retain the biological differences while mitigating the disparities caused by different acquisition protocols.
Here are some key details regarding the competition:
Dataset: Thanks to our colleagues at QIMR Berghofer Medical Research Institute, we have curated a newly released dataset comprising 100 subjects with paired data from two distinct diffusion acquisition protocols. The dataset is available for download here https://vanderbilt.app.box.com/s/owijt2mo2vhrp3rjonf90n3hoinygm8z/folder/208448607516.
Evaluation: The submitted harmonized images will be evaluated based on their similarity metrics for microstructure, macrostructure, and connectomics.
Prizes: We will be awarding generous prizes to the top performers. Participating teams will also be included as co-authors on the subsequent journal publication.
Registration: One person from the team should fill out this form. We have set up a box.com folder where you will upload your submission. There is no fee for challenge entry. You do not need to attend MICCAI to participate, but we would love to see you in Vancouver.
Eligibility: This competition is open to anyone except the organizing committee.
To register for the competition and gain access to the dataset and detailed guidelines, please visit our competition website.
We highly encourage you to participate in this captivating competition, as your expertise and insights can contribute significantly to the advancement of image harmonization techniques in medical research. By joining this event, you will not only have the chance to demonstrate your skills but also make a meaningful impact on the field.
Thank you for considering this opportunity, and we look forward to your contributions in the QuantConn Challenge!
The QuantConn Organization Team include:
Nancy Newlin, Computer Science, Vanderbilt University
Kurt Schilling, Radiology, Vanderbilt University School of Medicine
Neda Jahanshad, Keck School of Medicine, University of Southern California
Daniel Moyer, Computer Science, Vanderbilt University
Eleftherios Garyfallidis, Intelligent Systems Engineering, Indiana University
Bennett Landman, Electrical and Computer Engineering, Vanderbilt University
Serge Koudoro, Intelligent Systems Engineering, Indiana University
Bramsh Chandio, Keck School of Medicine, University of Southern California
Dataset contributions:
Margaret J. Wright, Lachlan Strike
Strike, Lachlan T. and Blokland, Gabriella A.M. and Hansell, Narelle K. and Martin, Nicholas G. and Toga, Arthur W. and Thompson, Paul M. and de Zubicaray, Greig I. and McMahon, Katie L. and Wright, Margaret J. (2023). Queensland Twin IMaging (QTIM). OpenNeuro. [Dataset] doi: doi:10.18112/openneuro.ds004169.v1.0.7
Should you have any questions or require further clarification, please do not hesitate to reach out to us at nancy.r...@vanderbilt.edu. We are here to support you throughout the competition journey.
Thank you all & Happy Hacking!,