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Research ArticlePediatric Neuroimaging

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar, Deep Gandhi, Karthik Viswanathan, Wenxin Tu, Debanjan Haldar, Sina Bagheri, Hannah Anderson, Shuvanjan Haldar, Phillip B. Storm, Adam Resnick, Jeffrey B. Ware, Ali Nabavizadeh and Anahita Fathi Kazerooni
American Journal of Neuroradiology August 2024, 45 (8) 1081-1089; DOI: https://doi.org/10.3174/ajnr.A8293
Arastoo Vossough
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
cDepartment of Radiology (A.V.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Nastaran Khalili
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Ariana M. Familiar
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Deep Gandhi
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Karthik Viswanathan
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Wenxin Tu
dCollege of Arts and Sciences (W.T.), University of Pennsylvania, Philadelphia, Pennsylvania
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Debanjan Haldar
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Sina Bagheri
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
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Hannah Anderson
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Shuvanjan Haldar
eSchool of Engineering (S.H.), Rutgers University, New Brunswick, New Jersey
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Phillip B. Storm
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
fDepartment of Neurosurgery (P.B.S., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Adam Resnick
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Jeffrey B. Ware
bDepartment of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
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Ali Nabavizadeh
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
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Anahita Fathi Kazerooni
aFrom the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
fDepartment of Neurosurgery (P.B.S., A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
gCenter for AI & Data Science for Integrated Diagnostics (A.F.K.), University of Pennsylvania, Philadelphia, Pennsylvania
hCenter for Biomedical Image Computing and Analytics (A.F.K.), University of Pennsylvania, Philadelphia, Pennsylvania
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Abstract

BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images.

MATERIALS AND METHODS: Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method.

RESULTS: The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively.

CONCLUSIONS: The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

ABBREVIATIONS:

AI
artificial intelligence
BraTS
Brain Tumor Segmentation Challenge
CBTN
Children’s Brain Tumor Network
CC
cystic component
CNN
convolutional neural network
DMG/DIPG
diffuse midline glioma/diffuse intrinsic pontine glioma
ED
edema
ET
enhancing tumor
NET
nonenhancing tumor
TC
tumor core
WT
whole tumor
  • © 2024 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 45 (8)
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Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar, Deep Gandhi, Karthik Viswanathan, Wenxin Tu, Debanjan Haldar, Sina Bagheri, Hannah Anderson, Shuvanjan Haldar, Phillip B. Storm, Adam Resnick, Jeffrey B. Ware, Ali Nabavizadeh, Anahita Fathi Kazerooni
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors
American Journal of Neuroradiology Aug 2024, 45 (8) 1081-1089; DOI: 10.3174/ajnr.A8293

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nnU-Net vs DeepMedic for PEDS BT Segmentation
Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar, Deep Gandhi, Karthik Viswanathan, Wenxin Tu, Debanjan Haldar, Sina Bagheri, Hannah Anderson, Shuvanjan Haldar, Phillip B. Storm, Adam Resnick, Jeffrey B. Ware, Ali Nabavizadeh, Anahita Fathi Kazerooni
American Journal of Neuroradiology Aug 2024, 45 (8) 1081-1089; DOI: 10.3174/ajnr.A8293
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