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Updated: Jan 11, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Anatomy-informed deep learning and radiomics for neurofibroma segmentation in whole-body MRI
Georgii Kolokolnikov1, Marie-Lena Schmalhofer2, Lennart Well2
1Institute for Applied Medical Informatics, Institute of Computational Neuroscience, and Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany.
Background And Objectives:
Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by the development of multiple neurofibromas (NFs) throughout the body. Accurate segmentation of these tumors in whole-body magnetic resonance imaging (WB-MRI) is critical for quantifying tumor burden and clinical decision-making. This study aims to develop a pipeline for NF segmentation in fat-suppressed T2-weighted WB-MRI that incorporates anatomical context and radiomics to improve accuracy and specificity.
Methods:
The proposed pipeline consists of three stages: (1) anatomy segmentation using MRSegmentator and refinement with a high-risk NF zone; (2) NF segmentation using an ensemble of 3D anisotropic anatomy-informed U-Nets; and (3) tumor candidate classification using radiomic features to filter false positives. The study used 109 WB-MRI scans from 74 NF1 patients, divided into training and three test sets representing in-domain (3T), domain-shifted (1.5T), and low tumor burden scenarios. Evaluation metrics included per-scan and per-tumor Dice Similarity Coefficient (DSC), Volume Overlap Error (VOE), Absolute Relative Volume Difference (ARVD), and per-scan F1 score. Statistical significance was assessed using Wilcoxon signed-rank tests with Bonferroni correction.
Results:
On the in-domain test set, the proposed ensemble of 3D anisotropic anatomy-informed U-Nets with tumor candidate classification achieved a per-scan DSC of 0.64, outperforming 2D nnU-Net (DSC: 0.52) and 3D full-resolution nnU-Net (DSC: 0.54). Performance was maintained on the domain-shift test set (DSC: 0.51) but declined on low tumor burden cases (DSC: 0.23). Preliminary inter-reader variability analysis showed model-to-expert agreement (DSC: 0.67-0.69) comparable to inter-expert agreement (DSC: 0.69).
Conclusions:
The proposed pipeline achieves the highest performance among established methods for automated NF segmentation in WB-MRI and approaches expert-level consistency. The integration of anatomical context and radiomics enhances robustness. Nonetheless, segmentation performance decreases in low tumor burden scenarios, indicating a key area for future methodological improvements. Additionally, the limited inter-reader agreement observed among experts underscores the inherent complexity and ambiguity of the NF segmentation task.
