Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI
The Brain Tumor Sequence Registration (BraTS-Reg) challenge is now accepted to run in conjunction with the ISBI 2022 scientific conference. We will update this page with all related information in the next few days.
Registration of Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem. We organize the first Brain Tumor Sequence Registration (BraTS-Reg) challenge, focusing on estimating correspondences between baseline pre-operative and follow up scans of the same patient diagnosed with a brain glioma. The BraTS-Reg challenge intends to establish a benchmark environment for deformable registration algorithms. The dataset associated with this challenge comprises de-identified multi-institutional multi-parametric MRI (mpMRI) data, curated for each scan’s size and resolution, according to a common anatomical template. The clinical experts of our team have generated extensive annotations of landmarks points within the scans. The “training data” along with these ground truth annotations will be released to participants to design their registration methods, whereas annotations of the “validation” and “test” data will be withheld by the organizers and used to evaluate the containerized algorithms of the participants. We will conduct the quantitative evaluation of the submitted algorithms using several metrics, such as Median Absolute Error and Robustness.
(All deadlines are for 23:59 Eastern Time)
|15 Nov 2021||Challenge Website goes live|
|25 Nov 2021||Challenge Design Document is available on arXiv|
|06 Dec 2021||
Training Phase starts: (Release of training data + associated ground truth).
|10 Jan 2022||Validation phase (Release of validation data, with hidden ground truth)|
|14 Jan 2022||Evaluation Platform goes live|
|11 Feb 2022||Submission of containerized algorithms & short papers, reporting method & results.|
|01 Mar 2022||Contacting top-ranked teams, to prepare slides for oral presentation at ISBI.|
|28-31 Mar 2022||Challenge at ISBI. Presentation of top-ranked methods.|
|(All deadlines are for 23:59 Eastern Time)|
Registration of baseline pre-operative (treatment-naïve) and follow-up brain tumor MRI scans is challenging, yet a clinically important task for a multitude of reasons. Brain tissue shows heavy deformations induced by the apparent tumor (also known as mass effect) that following its resection are relaxed due to the relieving pressure from the resected tissue. Such deformations affect the whole brain (including the lateral ventricles) and are not limited to the vicinity of the tumor. This is particularly important as the relationship of the tumor to the lateral ventricles and the deformations to the rest of the brain tissue are important factors in prognosis and treatment planning. Further changes in the peritumoral edematous/infiltrated tissue, potential tumor recurrence, as well as treatment related changes, also affect the brain tissue elasticity. The resected tissue/tumor also relates to missing correspondences, and inconsistent intensity profiles between the follow up and the baseline pre-operative scans.
Taking all the above into consideration, finding spatial correspondences between two longitudinal scans of brain tumor patients, i.e., the registration between the baseline pre-operative and follow-up MRI scans, can advance our mechanistic understanding for these tumors. Specifically, for tumor infiltration and potential recurrence, further contributing in the generation of predictive modelling for related pathophysiological processes, but also in understanding biophysical dynamic and plasticity characteristic of brain tissues, as well as for neurosurgical planning.
The registration between pre-operative and follow-up MRI scans of brain glioma patients, is important yet challenging task. In this challenge, participants are invited to develop deformable image registration algorithm by using the provided clinically acquired training data and the annotations done by our expert clinical neuroradiologists.
The evaluation of the registration between the two scans will be based on manually seeded landmarks (ground truth) in both the pre-operative and the follow-up scans. The performance will be quantitatively evaluated in terms of Median Absolute Error (MAE) and Robustness.
We have identified, curated, and pre-processed retrospective multi-institutional data. The data comprises of pairs of pre-operative baseline and follow-up MRI brain scans (each pair being of the same patient) diagnosed and treated for glioma. The exact multi-parametric MRI (mpMRI) sequences of each timepoint are i) native (T1) and ii) contrastenhancedT1-weighted (T1-CE), iii) T2-weighted and iv) T2Fluid Attenuated Inversion Recovery (FLAIR).
In training phase, participants will be provided with the ground truth annotations along with the MRI data. The ground truth consists of location of some unique landmark points found in baseline scan and their corresponding locations in follow-up scan to develop the deformable registration algorithms.
These landmarks are defined on anatomical markers such as blood vessel bifurcations, the anatomical shape of the cortex, and anatomical landmarks of the midline of the brain. The total number of landmarks vary from case to case and across all cases in the range of 6-50 per scan.
The validation data will be provided to the participants as scan pairs of baseline and follow-up with landmarks provided only for the follow-up scan. Participants will submit coordinates of warped landmark locations in the baseline scan. Also, they will be called to upload their method in a containerized way for evaluation on testing data.
Training Data availability (6 Dec 2021)
Register for the BraTS_Reg challenge, to get access to the NIFTI, skull-stripped, and annotated training data.
Validation Data availability (Jan 2022)
An independent set of validation scans will be made available to the participants, with the intention to allow them assess the generalizability of their methods in unseen data, via the official evaluation platforms.
Short Paper submission deadline (Feb 2022)
Participants will have to evaluate their methods on the training and validation datasets, and submit short paper describing their method and results. The organizers will review the paper for sufficient details required to understand and reproduce the algorithm. The challenge participants will be given a chance with the option to extend their individual papers, and hence publish their methods in IEEE Xplore proceedings (Pending exact process by the ISBI organizers).
Testing phase (Feb 2022)
The BraTS-Reg test data will not be made available to the participating teams. Participants will need to submit their method in a containerized form. (more details will follow up about how to create containers).
Oral Presentations at ISBI (Mar 2022)
The top-ranked participants of the validation phase that have submitted a short paper and containerized algorithm, will be contacted by March to prepare slides for orally presenting their method in ISBI 2022. The final results of the challenge will also be reported at ISBI.
Joint post-conference journal paper
Finally, we intend to coordinate a journal meta-analysis manuscript in one of the reputed journals in the domain by extending this preprint appropriately to describe the challenge design, data, clinical relevance, and summarizing the results and insights of the challenge.
Challenge data may be used for all purposes, provided that the challenge is appropriately referenced using the citation given at the bottom of this page.
To request the training and the validation data of the BraTS-Reg 2022 challenge, please follow the registration steps below. Please note that the i) training data includes ground truth annotations, ii) validation data does not include annotations, and iii) testing data are not available to the public or to the challenge participants.
The evaluation platform for the BraTS-Reg 2022 challenge is currently under construction. The platform link will appear here by the date given in the "Important Dates" section.
You are free to use and/or refer to the BraTS-Reg datasets in your own research, provided that you always cite the following manuscript:
Feel free to send any communication related to the BraTS-Reg challenge to email@example.com
- Bhakti Baheti (University of Pennsylvania)
- Diana Waldmannstetter (University of Zurich and Technical University of Munich)
- Satrajit Chakrabarty (Washington University in Saint Louis)
- Hamed Akbari (University of Pennsylvania)
- Aristeidis Sotiras (Washington University in Saint Louis)
- Bjoern Menze (University of Zurich and Technical University of Munich)
- Spyridon Bakas (University of Pennsylvania)
Clinical Experts & Data Contributors
- Michel Bilello (University of Pennsylvania)
- Benedikt Wiestler (Technical University of Munich)
- Syed Abidi (Washington University in Saint Louis)
- Mina Mousa (Washington University in Saint Louis)
- Evan Calabrese (University of California San Francisco)
- Jeffrey Rudie (University of California San Francisco)
- Javier Villanueva-Meyer (University of California San Francisco)
- Daniel S. Marcus (Washington University in Saint Louis)
- Christos Davatzikos (University of Pennsylvania)