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Brain Pre-surgical white matter Tractography Mapping challenge (BrainPTM) 2021

Background

The accurate mapping of white matter (WM) tracts is critical to the success of neuro-surgical planning
and navigation. For the last 15 years, this task was exclusively based on white matter tractography
algorithms [1]. Deterministic algorithms, that tracks the principal direction of diffusion (PDD), as well as
probabilistic approaches, that generate tracts in a random walk process, have both been applied for this
purpose [1]. In both cases, the manual delineation of accurate seeding regions is performed, requiring specific
neuroanatomical knowledge and significant amount of time. Moreover, the tract generation itself may
require significant computing time or power, as probabilistic algorithms are generally needed to obtain a
sufficiently complete reconstruction of the motor and optic radiation tracts using clinical diffusion weighted
imaging scans [1].

While the fiber tracts representation is useful for brain research, as it enables quantitative measurementssampled along the tracts, neuro-surgeons rather need a volumetric segmentation of the tracts for mappingpurpose. Automating and accelerating this process would significantly reduce the amount of time spent onneuro-surgical planning phase and it might improve the accuracy of tracts mapping which is crucial for brain surgery.  Recent progresses in multi-modal deep neural networks, suggest these may benefit to whitematter tracts mapping, either by automating seeding regions generation [2], or by  direct segmentation of tract voxels [3]. 
In this context, the CILAB organizes this challenge to encourage the development of  machine learning approaches  

to white matter tracts mapping in clinical  brain MRI scans. Significant progress in this area
will improve the  neuro-surgical planning procedure in terms of time, accuracy, and robustness.

Challenge

In this challenge we ask the participants to perform direct white matter tracts mapping in clinical brain
MRI scans we provide. The data that is provided consists of 75 cases (patients referred for brain tumor
removal) that were acquired at Sheba Medical Center at Tel HaShomer, Israel [2]. Patient pathologies
include oligodendrogliomas , astrocytomas, glioblastomas and cavernomas, on first occurrence or in a
post-surgical recurrence. According to the neuro-radiologist's estimation, the tumor volumes ranged
from 4 (cavernoma) to 60 [cm^3] (glioblastoma multiforme). Also, different levels of edema are present
around the dataset tumors, from inexistent to very significant.

Along with each case both T1 Structural and Diffusion Weighted modalities are provided. For 60 cases
(training) semi-manual white matters tracts mapping is provided in the form of binary segmentation maps.
For the rest 15 cases (test) no tracts annotations are provided as these will be used for participants
algorithms evaluation.

More details in participation section.

Data usage agreement

Participants cannot share the data, cannot use it for any commercial purpose. If this dataset or part of it is used in a published
paper (as well as test set evaluation results from the leaderboard) please cite the following papers:

@article{avital2019neural,
  title={Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography},
  author={Avital, Itzik and Nelkenbaum, Ilya and Tsarfaty, Galia and Konen, Eli and Kiryati, Nahum and Mayer, Arnaldo},
  journal={IEEE transactions on medical imaging},
  volume={39},
  number={5},
  pages={1655--1667},
  year={2019},
  publisher={IEEE}
}

@inproceedings{nelkenbaum2020automatic,
  title={Automatic Segmentation of White Matter Tracts Using Multiple Brain MRI Sequences},
  author={Nelkenbaum, Ilya and Tsarfaty, Galia and Kiryati, Nahum and Konen, Eli and Mayer, Arnaldo},
  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  pages={368--371},
  year={2020},
  organization={IEEE}
}
Contacts

The challenge is organized by the CILAB @ Sheba medical center, Israel

If you have any questions, please contact:

References

[1] A. S. Bick, A. Mayer, and N. Levin, “From research to clinical practice: implementation of functional
magnetic imaging and white matter tractography in the clinical environment,” Journal of the neurological
sciences, vol. 312, no. 1-2, pp. 158–165, 2012.

[2] I. Avital, I. Nelkenbaum, G. Tsarfaty, E. Konen, N. Kiryati, and A. Mayer, “Neural segmentation of seeding
rois (srois) for pre-surgical brain tractography,” IEEE Transactions on Medical Imaging, vol. 39, no. 5, pp.
1655–1667, 2019.

[3] I. Nelkenbaum, G. Tsarfaty, N. Kiryati, E. Konen, and A. Mayer, 2020, April. Automatic Segmentation of White
Matter Tracts Using Multiple Brain MRI Sequences. In 2020 IEEE 17th International Symposium on Biomedical
Imaging (ISBI), pp. 368-371. IEEE.