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Neurooncol Adv
Neurooncol Adv
noa
Neuro-Oncology Advances
2632-2498
Oxford University Press US

10.1093/noajnl/vdae090.037
vdae090.037
Final Category: Data Sciences/AI Advances
AcademicSubjects/MED00300
AcademicSubjects/MED00310
DSAI-05 THE BRAIN TUMOR SEGMENTATION (BRATS-METS) CHALLENGE 2023: BRAIN METASTASIS SEGMENTATION ON PRE-TREATMENT MRI
Tahon Nourel hoda Radiology Department, University of Missouri, Columbia, Columbia/MO, USA

Ashraf Nader Yale University, Department of Radiology, New Haven/CT, USA

Moawad Ahmed Trinity Health Mid Atlantic Hospitals, Darby/PA, USA

Janas Anastasia Yale University, Department of Radiology, New Haven/CT, USA

Baid Ujjwal Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis/IN, USA

Saluja Rachit Cornell University, Ithaca/NY, USA

Velichko Yuri Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago/IL, USA

Ramakrishnan Divya Yale University, Department of Radiology, New Haven/CT, USA

Krantchev Kiril ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven/CT, USA

Rudie Jeffrey University of California San Diego, San Diego/CA, USA

Bakas Spyridon Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University., Indianapolis/IN, USA

Aboian Mariam Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia/PA, USA

8 2024
02 8 2024
02 8 2024
6 Suppl 1 2024 SNO/ASCO CNS Metastases Conference i12i12
© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

PURPOSE

Clinical monitoring of metastatic disease to the brain using magnetic resonance imaging (MRI) can be laborious and time-consuming, particularly when multiple small metastases are involved and assessments are performed manually.

METHODS AND MATERIALS

The BraTS-METS 2023 dataset is acquired from varying MRI imaging quality across different vendors. The scans are pre-processed using different algorithms refined by a pool of annotators with different expertise. Two independent board-certified neuroradiologists finally reviewed the dataset. The datasets are divided into Training, validation, and testing. ASNR-MICCAI BraTS-METS 2023 challenge was evaluated based on Dice scores and Hausdorff distance for each lesion, including the whole tumor, enhancing tumor, and tumor core.

RESULTS

We received > 2500 multi-parametric MRIs from 12 different institutions, which underwent preprocessing to wipe out all PHI, reorient, and be consistent with all BraTS space and header. Initial raw data were pre-segmented with three different algorithms and fused together to get a consensus pre-segmentation file. A pool of 150 annotators (with different experience and training levels in radiology) and 50 board-certified attendees were recruited through ASNR mass calls for volunteer announcements, Segmentation workshops, and the ASNR annual meeting campaign. Studies are first assigned to annotators, reviewed by 1-2 board-certified neuroradiologists, and then reviewed by a single senior neuroradiologist for consistency and quality control final check. Finalized segmentation files underwent quantitative QC check to ensure harmonized imaging parameters, headers, and masks. Training and Validation datasets are made available to the public through the BraTS-METS 2023 website. The whole project is part of the TCIA/NCI moonshot program.

CONCLUSION

: The MICCAI-ASNR BraTS-METS Challenge is an important initiative for developing accurate segmentation algorithms to detect small brain metastasis. It includes multi-institutional and international datasets in order to develop a general model applied to all patients with brain metastasis.
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