Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

Author:

Pemberton Hugh G.,Wu Jiaming,Kommers Ivar,Müller Domenique M. J.,Hu Yipeng,Goodkin Olivia,Vos Sjoerd B.,Bisdas Sotirios,Robe Pierre A.,Ardon Hilko,Bello Lorenzo,Rossi Marco,Sciortino Tommaso,Nibali Marco Conti,Berger Mitchel S.,Hervey-Jumper Shawn L.,Bouwknegt Wim,Van den Brink Wimar A.,Furtner Julia,Han Seunggu J.,Idema Albert J. S.,Kiesel Barbara,Widhalm Georg,Kloet Alfred,Wagemakers Michiel,Zwinderman Aeilko H.,Krieg Sandro M.,Mandonnet Emmanuel,Prados Ferran,de Witt Hamer Philip,Barkhof Frederik,Eijgelaar Roelant S.

Abstract

AbstractThis study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals’ data. All models’ median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74–0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.

Funder

National Institute for Health Research (NIHR) biomedical research centre at UCLH

Hanarth Fonds

Innovative Medical Devices Initiative program

KWF Kankerbestrijding

Guarantors of Brain fellowship

Biomedical Research Centre initiative at University College London Hospitals

Topsector Life Sciences and Health

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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