Universität zu Köln

Perkuhn, Michael, Stavrinou, Pantelis ORCID: 0000-0001-8653-1395, Thiele, Frank, Shakirin, Georgy, Mohan, Manoj, Garmpis, Dionysios, Kabbasch, Christoph and Borggrefe, Jan ORCID: 0000-0003-2908-7560 (2018). Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine. Invest. Radiol., 53 (11). S. 647 - 655. PHILADELPHIA: LIPPINCOTT WILLIAMS & WILKINS. ISSN 1536-0210

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Abstract

Objectives: The aims of this study were, first, to evaluate a deep learning-based, automatic glioblastoma (GB) tumor segmentation algorithm on clinical routine data from multiple centers and compare the results to a ground truth, manual expert segmentation, and second, to evaluate the quality of the segmentation results across heterogeneous acquisition protocols of routinely acquired clinical magnetic resonance imaging (MRI) examinations from multiple centers. Materials and Methods: The data consisted of preoperative MRI scans (T1, T2, FLAIR, and contrast-enhanced [CE] T1) of 64 patients with an initial diagnosis of primary GB, which were acquired in 15 institutions with varying protocols. All images underwent preprocessing (coregistration, skull stripping, resampling to isotropic resolution, normalization) and were fed into an independently trained deep learning model based on DeepMedic, a multilayer, multiscale convolutional neural network for detection and segmentation of tumor compartments. Automatic segmentation results for the whole tumor, necrosis, and CE tumor were compared with manual segmentations. Results: Whole tumor and CE tumor compartments were correctly detected in 100% of the cases; necrosis was correctly detected in 91% of the cases. A high segmentation accuracy comparable to interrater variability was achieved for the whole tumor (mean dice similarity coefficient [DSC], 0.86 +/- 0.09) and CE tumor (DSC, 0.78 +/- 0.15). The DSC for tumor necrosis was 0.62 +/- 0.30. We have observed robust segmentation quality over heterogeneous image acquisition protocols, for example, there were no correlations between resolution and segmentation accuracy of the single tumor compartments. Furthermore, no relevant correlation was found between quality of automatic segmentation and volume of interest properties (surface-to-volume ratio and volume). Conclusions: The proposed approach for automatic segmentation of GB proved to be robust on routine clinical data and showed on all tumor compartments a high automatic detection rate and a high accuracy, comparable to interrater variability. Further work on improvements of the segmentation accuracy for the necrosis compartments should be guided by the evaluation of the clinical relevance. Therefore, we propose this approach as a suitable building block for automatic tumor segmentation to support radiologists or neurosurgeons in the preoperative reading of GB MRI images and characterization of primary GB.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Perkuhn, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Stavrinou, Pantelis UNSPECIFIED orcid.org/0000-0001-8653-1395 UNSPECIFIED Thiele, Frank UNSPECIFIED UNSPECIFIED UNSPECIFIED Shakirin, Georgy UNSPECIFIED UNSPECIFIED UNSPECIFIED Mohan, Manoj UNSPECIFIED UNSPECIFIED UNSPECIFIED Garmpis, Dionysios UNSPECIFIED UNSPECIFIED UNSPECIFIED Kabbasch, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Borggrefe, Jan UNSPECIFIED orcid.org/0000-0003-2908-7560 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-168386
DOI:	10.1097/RLI.0000000000000484
Journal or Publication Title:	Invest. Radiol.
Volume:	53
Number:	11
Page Range:	S. 647 - 655
Date:	2018
Publisher:	LIPPINCOTT WILLIAMS & WILKINS
Place of Publication:	PHILADELPHIA
ISSN:	1536-0210
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language BRAIN; RADIOMICS Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/16838