医学
卷积神经网络
一致性
人工智能
一致相关系数
分割
数据集
试验装置
深度学习
模式识别(心理学)
Sørensen–骰子系数
相似性(几何)
卡帕
科恩卡帕
核医学
放射外科
放射科
图像分割
统计
机器学习
计算机科学
放射治疗
内科学
图像(数学)
数学
几何学
作者
Khaled Bousabarah,Maximilian I. Ruge,Julia-Sarita Brand,Mauritius Hoevels,Daniel Rueß,Jan Borggrefe,Nils Große Hokamp,Veerle Visser‐Vandewalle,David Maintz,Harald Treuer,Martin Köcher
标识
DOI:10.1186/s13014-020-01514-6
摘要
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.
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