Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and
  Pediatrics
Identifying key pathological features in brain MRIs is crucial for the
long-term survival of glioma patients. However, manual segmentation is
time-consuming, requiring expert intervention and is susceptible to human
error. Therefore, significant research has been devoted to developing machine
learning methods that can accurately segment tumors in 3D multimodal brain MRI
scans. Despite their progress, state-of-the-art models are often limited by the
data they are trained on, raising concerns about their reliability when applied
to diverse populations that may introduce distribution shifts. Such shifts can
stem from lower quality MRI technology (e.g., in sub-Saharan Africa) or
variations in patient demographics (e.g., children). The BraTS-2024 challenge
provides a platform to address these issues. This study presents our
methodology for segmenting tumors in the BraTS-2024 SSA and Pediatric Tumors
tasks using MedNeXt, comprehensive model ensembling, and thorough
postprocessing. Our approach demonstrated strong performance on the unseen
validation set, achieving an average Dice Similarity Coefficient (DSC) of 0.896
on the BraTS-2024 SSA dataset and an average DSC of 0.830 on the BraTS
Pediatric Tumor dataset. Additionally, our method achieved an average Hausdorff
Distance (HD95) of 14.682 on the BraTS-2024 SSA dataset and an average HD95 of
37.508 on the BraTS Pediatric dataset. Our GitHub repository can be accessed
here: Project Repository :
https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics
6746df9e92de61cf7ce4271f
Sarim Hashmi
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2024-11-27T22:19:50.065Z
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SARIM HASHMI
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Sarim-Hash
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Juan Lugo
admin_assigned
2024-11-28T12:29:10.569Z
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juan lugo
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jlugopul
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Abdelrahman Elsayed
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2024-11-27T22:19:48.340Z
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Abdelrahman Amr El Sayed
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pythn
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Dinesh Saggurthi
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Mohammed Elseiagy
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2024-11-28T12:29:01.617Z
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Mohammed Elseiagy
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MohammedElseiagy
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Alikhan Nurkamal
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2024-11-28T12:28:56.167Z
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Alikhan Nurkamal
user
alikhann03
6746df9e92de61cf7ce42725
Jaskaran Walia
claimed_verified
2024-11-28T21:15:27.582Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/82P69_hL1VcnMPdNDrCim.png
Jaskaran Singh Walia
user
karanwxlia
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Fadillah Adamsyah Maani
admin_assigned
2024-11-28T12:28:45.028Z
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Fadillah Adamsyah Maani
user
adamsyah
6746df9e92de61cf7ce42727
Mohammad Yaqub
admin_assigned
2024-11-28T12:28:38.400Z
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Mohammad Yaqub Bin Sheikh Mahmoud
user
shxljit
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and
  Pediatrics
Identifying key pathological features in brain MRIs is crucial for the
long-term survival of glioma patients. However, manual segmentation is
time-consuming, requiring expert intervention and is susceptible to human
error. Therefore, significant research has been devoted to developing machine
learning methods that can accurately segment tumors in 3D multimodal brain MRI
scans. Despite their progress, state-of-the-art models are often limited by the
data they are trained on, raising concerns about their reliability when applied
to diverse populations that may introduce distribution shifts. Such shifts can
stem from lower quality MRI technology (e.g., in sub-Saharan Africa) or
variations in patient demographics (e.g., children). The BraTS-2024 challenge
provides a platform to address these issues. This study presents our
methodology for segmenting tumors in the BraTS-2024 SSA and Pediatric Tumors
tasks using MedNeXt, comprehensive model ensembling, and thorough
postprocessing. Our approach demonstrated strong performance on the unseen
validation set, achieving an average Dice Similarity Coefficient (DSC) of 0.896
on the BraTS-2024 SSA dataset and an average DSC of 0.830 on the BraTS
Pediatric Tumor dataset. Additionally, our method achieved an average Hausdorff
Distance (HD95) of 14.682 on the BraTS-2024 SSA dataset and an average HD95 of
37.508 on the BraTS Pediatric dataset. Our GitHub repository can be accessed
here: Project Repository :
https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics
A method using MedNeXt, model ensembling, and postprocessing achieves strong performance in tumor segmentation for the BraTS-2024 SSA and Pediatric Tumors challenges, demonstrating reliability across diverse populations.
MedNeXt model ensembling Dice Similarity Coefficient Hausdorff Distance