Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists

Authors

Qi Wan
Jisoo Kim
Clifford Lindsay
Xin Chen
Jing Li
J Bryan Iorgulescu
Raymond Y Huang
Chenxi Zhang
David Reardon
Geoffrey S Young
Lei Qin

Publication Date

8-1-2024

Journal

Journal of Imaging Informatics in Medicine

Abstract

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic research and clinical management, but very time-consuming. Fully automated tools for the segmentation of multi-sequence MRI are needed. We developed and pretrained a deep learning (DL) model using publicly available datasets A (n = 210) and B (n = 369) containing FLAIR, T2WI, and contrast-enhanced (CE)-T1WI. This was then fine-tuned with our institutional dataset (n = 197) containing ADC, T2WI, and CE-T1WI, manually annotated by radiologists, and split into training (n = 100) and testing (n = 97) sets. The Dice similarity coefficient (DSC) was used to compare model outputs and manual labels. A third independent radiologist assessed segmentation quality on a semi-quantitative 5-scale score. Differences in DSC between new and recurrent gliomas, and between uni or multifocal gliomas were analyzed using the Mann-Whitney test. Semi-quantitative analyses were compared using the chi-square test. We found that there was good agreement between segmentations from the fine-tuned DL model and ground truth manual segmentations (median DSC: 0.729, std-dev: 0.134). DSC was higher for newly diagnosed (0.807) than recurrent (0.698) (p < 0.001), and higher for unifocal (0.747) than multi-focal (0.613) cases (p = 0.001). Semi-quantitative scores of DL and manual segmentation were not significantly different (mean: 3.567 vs. 3.639; 93.8% vs. 97.9% scoring ≥ 3, p = 0.107). In conclusion, the proposed transfer learning DL performed similarly to human radiologists in glioma segmentation on both structural and ADC sequences. Further improvement in segmenting challenging postoperative and multifocal glioma cases is needed.

Keywords

Humans, Glioma, Brain Neoplasms, Magnetic Resonance Imaging, Neural Networks, Computer, Deep Learning, Adult, Radiologists, Image Interpretation, Computer-Assisted, Female, Male, Middle Aged, BraTS; Deep learning; Glioma; Segmentation

DOI

10.1007/s10278-024-01044-7

PMID

38383806

PMCID

PMC1130074

PubMedCentral® Posted Date

2-21-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes