BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
- URL: http://arxiv.org/abs/2407.08855v2
- Date: Tue, 16 Jul 2024 20:52:45 GMT
- Title: BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
- Authors: Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu, Debanjan Haldar, Zhifan Jiang, Anna Zapaishchykova, Julija Pavaine, Lubdha M. Shah, Blaise V. Jones, Nakul Sheth, Sanjay P. Prabhu, Aaron S. McAllister, Wenxin Tu, Khanak K. Nandolia, Andres F. Rodriguez, Ibraheem Salman Shaikh, Mariana Sanchez Montano, Hollie Anne Lai, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Hannah Anderson, Syed Muhammed Anwar, Alejandro Aristizabal, Sina Bagheri, Ujjwal Baid, Timothy Bergquist, Austin J. Borja, Evan Calabrese, Verena Chung, Gian-Marco Conte, James Eddy, Ivan Ezhov, Ariana M. Familiar, Keyvan Farahani, Deep Gandhi, Anurag Gottipati, Shuvanjan Haldar, Juan Eugenio Iglesias, Anastasia Janas, Elaine Elaine, Alexandros Karargyris, Hasan Kassem, Neda Khalili, Florian Kofler, Dominic LaBella, Koen Van Leemput, Hongwei B. Li, Nazanin Maleki, Zeke Meier, Bjoern Menze, Ahmed W. Moawad, Sarthak Pati, Marie Piraud, Tina Poussaint, Zachary J. Reitman, Jeffrey D. Rudie, Rachit Saluja, MIcah Sheller, Russell Takeshi Shinohara, Karthik Viswanathan, Chunhao Wang, Benedikt Wiestler, Walter F. Wiggins, Christos Davatzikos, Phillip B. Storm, Miriam Bornhorst, Roger Packer, Trent Hummel, Peter de Blank, Lindsey Hoffman, Mariam Aboian, Ali Nabavizadeh, Jeffrey B. Ware, Benjamin H. Kann, Brian Rood, Adam Resnick, Spyridon Bakas, Arastoo Vossough, Marius George Linguraru,
- Abstract summary: We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor (BraTS) challenge focused on pediatric brain tumors.
BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging.
The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework.
- Score: 44.64458075448
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.
Related papers
- Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation [47.119513326344126]
The BraTS-MEN-RT challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs.
Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space.
Target volume annotations adhere to established radiotherapy planning protocols.
arXiv Detail & Related papers (2024-05-28T17:25:43Z) - The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) [4.023596068202542]
CBTN--DIPGR-ASNR-MICCAI BraTS-PEDs challenge focused on pediatric brain tumors.
Five-year survival rate for high-grade gliomas in children is less than 20%.
arXiv Detail & Related papers (2024-04-23T13:15:22Z) - The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) [4.119582963064813]
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children.
The five-year survival rate for high-grade gliomas in children is less than 20%.
The BraTS-PEDs 2023 challenge focuses on the development of volumentric segmentation algorithms for pediatric brain glioma.
arXiv Detail & Related papers (2023-05-26T15:40:11Z) - The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023:
Intracranial Meningioma [4.435336201147607]
The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models.
Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI.
arXiv Detail & Related papers (2023-05-12T17:52:36Z) - Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS)
Benchmark [48.30502612686276]
Lung cancer is one of the deadliest cancers, and its effective diagnosis and treatment depend on the accurate delineation of the tumor.
Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability.
The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data.
In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique.
arXiv Detail & Related papers (2022-01-03T03:06:38Z) - Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric
MRI [0.0]
We propose a new aggregation of two deep learning frameworks namely, DeepSeg and nnU-Net for automatic glioblastoma recognition in pre-operative mpMRI.
Our ensemble method obtains Dice similarity scores of 92.00, 87.33, and 84.10 and Hausdorff Distances of 3.81, 8.91, and 16.02 for the enhancing tumor, tumor core, and whole tumor regions.
arXiv Detail & Related papers (2021-12-13T10:51:20Z) - Improving the Segmentation of Pediatric Low-Grade Gliomas through
Multitask Learning [0.1199955563466263]
We developed a segmentation model trained on magnetic resonance imaging (MRI) of pediatric patients with low-grade gliomas (pLGGs)
The proposed model utilizes deep Multitask Learning (dMTL) by adding tumor's genetic alteration classifier as an auxiliary task to the main network.
arXiv Detail & Related papers (2021-11-29T21:12:47Z) - H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd
Place Solution to BraTS Challenge 2020 Segmentation Task [96.49879910148854]
Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions.
We trained and evaluated our model on the Multimodal Brain Tumor Challenge (BraTS) 2020 dataset.
Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.
arXiv Detail & Related papers (2020-12-30T20:44:55Z) - Hybrid Attention for Automatic Segmentation of Whole Fetal Head in
Prenatal Ultrasound Volumes [52.53375964591765]
We propose the first fully-automated solution to segment the whole fetal head in US volumes.
The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture.
We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
arXiv Detail & Related papers (2020-04-28T14:43:05Z) - A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced
Cardiac Magnetic Resonance Imaging [90.29017019187282]
" 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset.
Analyse of the submitted algorithms using technical and biological metrics was performed.
Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
arXiv Detail & Related papers (2020-04-26T08:49:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.