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Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data
- Source :
- Radiation Oncology, Vol 15, Iss 1, Pp 1-9 (2020), Radiation Oncology (London, England)
- Publication Year :
- 2020
- Publisher :
- BMC, 2020.
-
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.
- Subjects :
- Target lesion
Male
lcsh:Medical physics. Medical radiology. Nuclear medicine
lcsh:R895-920
Radiosurgery
Convolutional neural network
Sensitivity and Specificity
lcsh:RC254-282
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Magnetic resonance imaging
Segmentation
Medicine
Humans
Radiology, Nuclear Medicine and imaging
Stereotactic radiosurgery
business.industry
Brain Neoplasms
Deep learning
Research
Radiotherapy Planning, Computer-Assisted
Brain metastasis
Pattern recognition
Middle Aged
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Concordance correlation coefficient
Oncology
030220 oncology & carcinogenesis
Test set
Radiographic Image Interpretation, Computer-Assisted
Female
False positive rate
Artificial intelligence
Neural Networks, Computer
business
Test data
Subjects
Details
- Language :
- English
- Volume :
- 15
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Radiation Oncology
- Accession number :
- edsair.doi.dedup.....8fef9d9cc2ebe0ee883ac58254c4b212