Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study
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Abstract
Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n = 151), validation (n = 43), and withheld internal test (n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.
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Although a few previous works addressed pediatric brain tumors, their automatic segmentation remains challenging due to a lack of training data and variation in heterogeneous histologic sub-regions.
“Although, a few previous works addressed pediatric brain tumors [10,14,20], their automatic segmentation remains challenging. This is due to a lack of training data and variation in heterogeneous histologic sub-regions including peritumoral edematous/invaded tissue, necrotic core, and enhancing tumor.”
From Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging (2024) · cites this paper as [10]
The introductory passage (labeled Abstract > Conclusions; page unavailable) describes a few earlier pediatric segmentation studies and limitations in their performance and ability to distinguish subregions. The Methods and Results (pages unavailable) also describe difficulty distinguishing enhancing tumor, non-enhancing tumor, and edema; the Discussion links limited edema examples to class imbalance. The paper does not establish a general lack of training data as the cause of the challenge, and its listed subregions do not include the claim’s necrotic core or invaded tissue. (LLM, conf=0.84)
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Сергеевна, Р. О., Пархоменко, Р. А., Sergeev, N. I., Bozhenko, K. V., Владимирович, П. П., & Solodkiy, V. А. (2024) · Digital Diagnostics · doi:10.17816/dd625382
Pediatric Brain Tumor Segmentation Using Multiresolution Fractal Deep Neural Network
Temtam, A. G., Sadique, M. S., Rahman, M. M., Farzana, W., & Iftekharuddin, K. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76163-8_30
Ghorbani, M., & Hasani, S. K. (2025) · The Cancer Review · doi:10.61882/tcr.202501.02.02
Few-Shot Brain Tumor Segmentation Using Transformer-Based Models with Explainable AI
Maryam, A., Kiani, H. M., & Asif, M. D. A. (2025) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt269104.2025.11360696
Brain Tumor Detection Using YOLOv8
Umamaheshwari, S., Subramanian, M. H. H., Kirthik., R., Hariram, C., Varshini, K. A. S., & Abirami, R. (2025) · International Conference of Advances in Engineering and Computing Technologies for Sustainable Development (AECTSD) · doi:10.1109/aectsd65988.2025.11411433
Learning Pediatric-Specific Brain Tumor Signatures with Multi-Region 3D U-Net Segmentation
Rizvi, F. R., Sharma, S., Gupta, S. K., Dandotiya, N., Dandotiya, A. S., & Gupta, P. (2025) · International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN) · doi:10.1109/emergin67762.2025.11450722
Paravila, A. S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-16365-3_7
- Authors
- Fathi Kazerooni, A., Arif, S., Madhogarhia, R., Khalili, N., Haldar, D., Bagheri, S., Familiar, A. M., Anderson, H., Haldar, S., Tu, W., Chul Kim, M., Viswanathan, K., Muller, S., Prados, M., Kline, C., Vidal, L., Aboian, M., Storm, P. B., Resnick, A. C., Ware, J. B., Vossough, A., Davatzikos, C., & Nabavizadeh, A.
- Affiliations
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia , Philadelphia, PA , USA
- Children's Hospital of Philadelphia (US)
- Rutgers, The State University of New Jersey (US)
- University of Pennsylvania (US)
- Department of Neurology and Pediatrics, University of California San Francisco , San Francisco, CA , USA
- University of California, San Francisco (US)
- Yale University (US)
- Corresponding author
- Nabavizadeh, Ali
- ORCID
- Venue
- Neuro-Oncology Advances
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-01-01
- Volume
- 5
- Issue
- 1
- Pages
- vdad027-vdad027
- ISSN
- 2632-2498 (ISSN-L)
- PMC
- PMC10084501
- OpenAlex
- W4327574677
- Cited by
- 55
- References
- 32
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 28 · 2023: 6
- FWCI
- 5.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Neuro-Oncology Advances (publishedVersion, cc-by)
- medRxiv (acceptedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- PubMed Central (submittedVersion)
- Funders
- National Institutes of Health (3U2CHL156291-02S1)
- National Center for Advancing Translational Sciences (TL1TR001880)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Keywords
- pediatric brain tumors, tumor segmentation, multiparametric MRI, DeepMedic, 3D convolutional neural networks, peritumoral edema, cystic tumor components, non-enhancing tumor, enhancing tumor, external validation
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