Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset

• URL: http://arxiv.org/abs/2507.22152v1
• Date: Tue, 29 Jul 2025 18:33:15 GMT
• Title: Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset
• Authors: A. Piffer, J. A. Buchner, A. G. Gennari, P. Grehten, S. Sirin, E. Ross, I. Ezhov, M. Rosier, J. C. Peeken, M. Piraud, B. Menze, A. Guerreiro Stücklin, A. Jakab, F. Kofler,
• Abstract summary: Brain tumours are the most common solid malignancies in children.<n>These tumours pose diagnostic and therapeutic challenges.<n>Deep learning (DL)-based segmentation offers promising tools for tumour delineation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background Brain tumours are the most common solid malignancies in children, encompassing diverse histological, molecular subtypes and imaging features and outcomes. Paediatric brain tumours (PBTs), including high- and low-grade gliomas (HGG, LGG), medulloblastomas (MB), ependymomas, and rarer forms, pose diagnostic and therapeutic challenges. Deep learning (DL)-based segmentation offers promising tools for tumour delineation, yet its performance across heterogeneous PBT subtypes and MRI protocols remains uncertain. Methods A retrospective single-centre cohort of 174 paediatric patients with HGG, LGG, medulloblastomas (MB), ependymomas, and other rarer subtypes was used. MRI sequences included T1, T1 post-contrast (T1-C), T2, and FLAIR. Manual annotations were provided for four tumour subregions: whole tumour (WT), T2-hyperintensity (T2H), enhancing tumour (ET), and cystic component (CC). A 3D nnU-Net model was trained and tested (121/53 split), with segmentation performance assessed using the Dice similarity coefficient (DSC) and compared against intra- and inter-rater variability. Results The model achieved robust performance for WT and T2H (mean DSC: 0.85), comparable to human annotator variability (mean DSC: 0.86). ET segmentation was moderately accurate (mean DSC: 0.75), while CC performance was poor. Segmentation accuracy varied by tumour type, MRI sequence combination, and location. Notably, T1, T1-C, and T2 alone produced results nearly equivalent to the full protocol. Conclusions DL is feasible for PBTs, particularly for T2H and WT. Challenges remain for ET and CC segmentation, highlighting the need for further refinement. These findings support the potential for protocol simplification and automation to enhance volumetric assessment and streamline paediatric neuro-oncology workflows.

Related papers

• MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
  We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation.<n>Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning.<n>Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
  arXiv  Detail & Related papers  (2025-03-18T15:39:44Z)
• Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma [0.2796197251957244]
  Multi-Task S-UNETR (MTSUNET) model is a novel foundation-based framework built on the BrainSegFounder model.<n>It simultaneously performs glioma segmentation, histological subtyping and neuroimaging subtyping.<n>It shows significant potential for advancing noninvasive, personalized glioma management by improving predictive accuracy and interpretability.
  arXiv  Detail & Related papers  (2025-03-10T01:27:09Z)
• Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation [6.14919256198409]
  We propose a deep learning-based ensemble approach that integrates state-of-the-art segmentation models.<n>Given the heterogeneous nature of the tumors present in the BraTS datasets, this approach enhances the precision and generalizability of segmentation models.
  arXiv  Detail & Related papers  (2024-12-05T12:00:00Z)
• T1-contrast Enhanced MRI Generation from Multi-parametric MRI for Glioma Patients with Latent Tumor Conditioning [1.581761125201628]
  Gadolinium-based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas. There is growing concern about GBCA toxicity. This study develops a deep-learning framework to generate T1-postcontrast (T1C) from pre-contrast multi- MRI.
  arXiv  Detail & Related papers  (2024-09-03T05:45:37Z)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.76736949127792]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge.<n>The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas.<n>The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Self-calibrated convolution towards glioma segmentation [45.74830585715129]
  We evaluate self-calibrated convolutions in different parts of the nnU-Net network to demonstrate that self-calibrated modules in skip connections can significantly improve the enhanced-tumor and tumor-core segmentation accuracy.
  arXiv  Detail & Related papers  (2024-02-07T19:51:13Z)
• CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images [42.771819949806655]
  We introduce CIMIL-CRC', a framework that solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort.
  arXiv  Detail & Related papers  (2024-01-29T12:56:11Z)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion [3.9562534927482704]
  We propose an end-to-end framework named Modality-Level Attention Fusion Network (MAF-Net) Our proposed MAF-Net is found to yield superior T1ce synthesis performance and accurate brain tumor segmentation.
  arXiv  Detail & Related papers  (2022-03-09T09:08:48Z)
• A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI [0.28675177318965034]
  Two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs) In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively. In the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types.
  arXiv  Detail & Related papers  (2022-01-14T10:24:47Z)
• PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation [4.573421102994323]
  We develop a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution. Our method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the abdominal organs.
  arXiv  Detail & Related papers  (2020-07-18T16:23:02Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.