NDLI: Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

Content Provider	Springer Nature : BioMed Central
Author	Bousabarah, Khaled Ruge, Maximilian Brand, Julia-Sarita Hoevels, Mauritius Rueß, Daniel Borggrefe, Jan Große Hokamp, Nils Visser-Vandewalle, Veerle Maintz, David Treuer, Harald Kocher, Martin
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.
Related Links	https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-020-01514-6.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13014-020-01514-6
Journal	Radiation Oncology
Issue Number	1
Volume Number	15
Language	English
Publisher	BioMed Central
Publisher Date	2020-04-20
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Radiotherapy Imaging Radiology Brain metastasis Magnetic resonance imaging Deep learning Segmentation Stereotactic radiosurgery
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.6/2023

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