Deep learning segmentation of pediatric brain tumors using diffusion-weighted MRI: towards an early, in-vivo classification pipeline
Clicks: 4
ID: 325149
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.9
/100
4 views
3 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #49 of 116 articles by views in Brain communications
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Diffusion-weighted MRI (DWI) can offer vital quantitative biomarkers to understand pediatric brain tumors to benefit clinical management. However, extraction of these markers requires delineation of the tumor margins, which is time-consuming, requires expertise and can still be highly variable. Automated approaches using deep learning to generate these tumor regions exist but, to our knowledge, few exist using only DWI as an input modality, with none in pediatrics. This retrospective study develops an automated segmentation approach, leveraging transfer-learning and multi-modal ensembling to tackle the issues specific to this DWI-only approach. Using the Imaging of Tumors study data we analysed data from 107 pediatric brain tumour patients. Using a 3D convolutional neural network (3D CNN), namely DeepMedic, to perform automatic segmentations, we demonstrate the benefit of these approaches for this task. We assess the accuracy of these predicted segmentations in comparison to the “ground truth” manual annotations, with a median Dice score of 0.63 achieved by the best performing model. The current study highlights the potential of this approach, to be implemented in future clinical decision support tools using DWI, but more work is needed to improve segmentation accuracy or establish current performance as ‘sufficient’ for the purposes that these segmentations are required.
| Reference Key |
openalex_W7203647591
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Daniel Griffiths-King, Timothy Mulvany, Heather E L Rose, Andrew C. Peet, John Apps, Theodoros N. Arvanitis, Jan Novák |
| Journal | Brain communications |
| Year | 2026 |
| DOI |
10.1093/braincomms/fcag317
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.