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Robust Deep Learning-based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training
Roelant S Eijgelaar1, Martin Visser1, Domenique M J Müller1
1Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands (R.S.E., M.v.H., M.G.W.); Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands (M.V., F.B., H.V., J.C.d.M.); Neurosurgical Center Amsterdam, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands (D.M.J.M., P.C.D.W.H.); Institutes of Neurology & Healthcare Engineering, University College London, London, England (F.B.); Faculty of Biology, Medicine & Health, Division of Cancer Sciences, University of Manchester and Christie NHS Trust, Manchester, England (M.v.H.); Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Humanitas Research Hospital, IRCCS, Milan, Italy (L.B., M.C.N., M.R., T.S.); Department of Neurologic Surgery, University of California-San Francisco, San Francisco, Calif (M.S.B., S.H.J.); Department of Neurosurgery, Medical University Vienna, Vienna, Austria (B.K., G.W.); Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria (J.F.); Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands (P.A.J.T.R.); and Department of Neurologic Surgery, Hôpital Lariboisière, Paris, France (E.M.).
Purpose:
To improve the robustness of deep learning-based glioblastoma segmentation in a clinical setting with sparsified datasets.
Materials And Methods:
In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55-73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012-2013) with similar imaging modalities of 634 patients (median age, 59 years; IQR, 49-69 years; 382 men) with glioblastoma from six hospitals were used. Expert tumor delineations on the postcontrast images were available, but for various clinical datasets, one or more sequences were missing. The convolutional neural network, DeepMedic, was trained on combinations of complete and incomplete data with and without site-specific data. Sparsified training was introduced, which randomly simulated missing sequences during training. The effects of sparsified training and center-specific training were tested using Wilcoxon signed rank tests for paired measurements.
Results:
A model trained exclusively on BraTS data reached a median Dice score of 0.81 for segmentation on BraTS test data but only 0.49 on the clinical data. Sparsified training improved performance (adjusted P < .05), even when excluding test data with missing sequences, to median Dice score of 0.67. Inclusion of site-specific data during sparsified training led to higher model performance Dice scores greater than 0.8, on par with a model based on all complete and incomplete data. For the model using BraTS and clinical training data, inclusion of site-specific data or sparsified training was of no consequence.
Conclusion:
Accurate and automatic segmentation of glioblastoma on clinical scans is feasible using a model based on large, heterogeneous, and partially incomplete datasets. Sparsified training may boost the performance of a smaller model based on public and site-specific data.Supplemental material is available for this article.Published under a CC BY 4.0 license.
