scholarly journals Peer Review #2 of "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks (v0.1)"

2019 ◽  
Keyword(s):  
Neural Networks ◽  
Lymph Node ◽  
Peer Review ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
Mr Lymphography ◽  
False Positive Reduction

scholarly journals Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8052
Author(s):  
Oscar A. Debats ◽  
Geert J.S. Litjens ◽  
Henkjan J. Huisman
Keyword(s):  
Prostate Cancer ◽  
Neural Networks ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
Detection System ◽  
False Positives ◽  
Cad System ◽  
Mr Lymphography

Purpose To investigate whether multi-view convolutional neural networks can improve a fully automated lymph node detection system for pelvic MR Lymphography (MRL) images of patients with prostate cancer. Methods A fully automated computer-aided detection (CAD) system had been previously developed to detect lymph nodes in MRL studies. The CAD system was extended with three types of 2D multi-view convolutional neural networks (CNN) aiming to reduce false positives (FP). A 2D multi-view CNN is an efficient approximation of a 3D CNN, and three types were evaluated: a 1-view, 3-view, and 9-view 2D CNN. The three deep learning CNN architectures were trained and configured on retrospective data of 240 prostate cancer patients that received MRL images as the standard of care between January 2008 and April 2010. The MRL used ferumoxtran-10 as a contrast agent and comprised at least two imaging sequences: a 3D T1-weighted and a 3D T2*-weighted sequence. A total of 5089 lymph nodes were annotated by two expert readers, reading in consensus. A first experiment compared the performance with and without CNNs and a second experiment compared the individual contribution of the 1-view, 3-view, or 9-view architecture to the performance. The performances were visually compared using free-receiver operating characteristic (FROC) analysis and statistically compared using partial area under the FROC curve analysis. Training and analysis were performed using bootstrapped FROC and 5-fold cross-validation. Results Adding multi-view CNNs significantly (p < 0.01) reduced false positive detections. The 3-view and 9-view CNN outperformed (p < 0.01) the 1-view CNN, reducing FP from 20.6 to 7.8/image at 80% sensitivity. Conclusion Multi-view convolutional neural networks significantly reduce false positives in a lymph node detection system for MRL images, and three orthogonal views are sufficient. At the achieved level of performance, CAD for MRL may help speed up finding lymph nodes and assessing them for potential metastatic involvement.

Convolutional Neural Networks for False Positive Reduction of Automatically Detected Cilia in Low Magnification TEM Images

2017 ◽  
pp. 407-418
Author(s):  
Anindya Gupta ◽  
Amit Suveer ◽  
Joakim Lindblad ◽  
Anca Dragomir ◽  
Ida-Maria Sintorn ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Reduction
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