Universität zu Köln

Bousabarah, Khaled, Ruge, Maximilian, Brand, Julia-Sarita, Hoevels, Mauritius, Ruess, Daniel, Borggrefe, Jan ORCID: 0000-0003-2908-7560, Hokamp, Nils Grosse, Visser-Vandewalle, Veerle, Maintz, David, Treuer, Harald and Kocher, Martin (2020). Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data. Radiat. Oncol., 15 (1). LONDON: BMC. ISSN 1748-717X

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Abstract

Introduction Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). Methods A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). Results A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. Conclusion Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Bousabarah, Khaled UNSPECIFIED UNSPECIFIED UNSPECIFIED Ruge, Maximilian UNSPECIFIED UNSPECIFIED UNSPECIFIED Brand, Julia-Sarita UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoevels, Mauritius UNSPECIFIED UNSPECIFIED UNSPECIFIED Ruess, Daniel UNSPECIFIED UNSPECIFIED UNSPECIFIED Borggrefe, Jan UNSPECIFIED orcid.org/0000-0003-2908-7560 UNSPECIFIED Hokamp, Nils Grosse UNSPECIFIED UNSPECIFIED UNSPECIFIED Visser-Vandewalle, Veerle UNSPECIFIED UNSPECIFIED UNSPECIFIED Maintz, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Treuer, Harald UNSPECIFIED UNSPECIFIED UNSPECIFIED Kocher, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-336724
DOI:	10.1186/s13014-020-01514-6
Journal or Publication Title:	Radiat. Oncol.
Volume:	15
Number:	1
Date:	2020
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1748-717X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Oncology; Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/33672