title: Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and
    Pediatrics
summary: 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
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paper_authors.name: Sarim Hashmi
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paper_authors.name: Abdelrahman Elsayed
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paper_authors.fullname: Abdelrahman Amr El Sayed
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paper_authors.user: pythn
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paper_authors.name: Dinesh Saggurthi
paper_authors._id: 6746df9e92de61cf7ce42723
paper_authors.name: Mohammed Elseiagy
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paper_authors.fullname: Mohammed Elseiagy
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paper_authors.user: MohammedElseiagy
paper_authors._id: 6746df9e92de61cf7ce42724
paper_authors.name: Alikhan Nurkamal
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paper_authors.fullname: Alikhan Nurkamal
paper_authors.type: user
paper_authors.user: alikhann03
paper_authors._id: 6746df9e92de61cf7ce42725
paper_authors.name: Jaskaran Walia
paper_authors.status: claimed_verified
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paper_authors.fullname: Jaskaran Singh Walia
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paper_authors.user: karanwxlia
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paper_authors.name: Fadillah Adamsyah Maani
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paper_authors.fullname: Fadillah Adamsyah Maani
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paper_authors.user: adamsyah
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paper_authors.name: Mohammad Yaqub
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paper_authors.fullname: Mohammad Yaqub Bin Sheikh Mahmoud
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paper_title: Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and
    Pediatrics
paper_summary: 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
paper_ai_keywords: MedNeXt model ensembling Dice Similarity Coefficient (DSC) Hausdorff Distance (HD95)