scholarly journals Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks

2018 ◽  
Vol 38 (9) ◽  
pp. 5437-5445 ◽  
Author(s):  
YU JIN CHA ◽  
WON IL JANG ◽  
MI-SOOK KIM ◽  
HYUNG JUN YOO ◽  
EUN KYUNG PAIK ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Metastases ◽  
Stereotactic Radiosurgery ◽  
Convolutional Neural Networks ◽  
Prediction Of Response

scholarly journals PO-0627: Prediction of radiosurgery response of brain metastases using convolutional neural networks

2017 ◽  
Vol 123 ◽  
pp. S328
Author(s):  
Y. Cha ◽  
M.S. Kim ◽  
C.K. Cho ◽  
H. Yoo ◽  
W.I. Jang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Metastases ◽  
Convolutional Neural Networks

scholarly journals Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Khaled Bousabarah ◽  
Maximilian Ruge ◽  
Julia-Sarita Brand ◽  
Mauritius Hoevels ◽  
Daniel Rueß ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Metastases ◽  
Convolutional Neural Networks ◽  
Clinical Data ◽  
Automated Segmentation ◽  
Deep Convolutional Neural Networks

Radiomics outperforms semantic features for prediction of response to stereotactic radiosurgery in brain metastases

2021 ◽  
Author(s):  
Robin Gutsche ◽  
Philipp Lohmann ◽  
Mauritius Hoevels ◽  
Daniel Ruess ◽  
Norbert Galldiks ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Stereotactic Radiosurgery ◽  
Semantic Features ◽  
Prediction Of Response

scholarly journals Contrast-Enhanced Black Blood MRI Sequence Is Superior to Conventional T1 Sequence in Automated Detection of Brain Metastases by Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1016
Author(s):  
Jonathan Kottlors ◽  
Simon Geissen ◽  
Hannah Jendreizik ◽  
Nils Große Hokamp ◽  
Philipp Fervers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Metastases ◽  
Convolutional Neural Networks ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Automated Detection ◽  
Lesion Detection ◽  
Black Blood ◽  
Mri Imaging ◽  
Contrast Enhanced

Background: in magnetic resonance imaging (MRI), automated detection of brain metastases with convolutional neural networks (CNN) represents an extraordinary challenge due to small lesions sometimes posing as brain vessels as well as other confounders. Literature reporting high false positive rates when using conventional contrast enhanced (CE) T1 sequences questions their usefulness in clinical routine. CE black blood (BB) sequences may overcome these limitations by suppressing contrast-enhanced structures, thus facilitating lesion detection. This study compared CNN performance in conventional CE T1 and BB sequences and tested for objective improvement of brain lesion detection. Methods: we included a subgroup of 127 consecutive patients, receiving both CE T1 and BB sequences, referred for MRI concerning metastatic spread to the brain. A pretrained CNN was retrained with a customized monolayer classifier using either T1 or BB scans of brain lesions. Results: CE T1 imaging-based training resulted in an internal validation accuracy of 85.5% vs. 92.3% in BB imaging (p < 0.01). In holdout validation analysis, T1 image-based prediction presented poor specificity and sensitivity with an AUC of 0.53 compared to 0.87 in BB-imaging-based prediction. Conclusions: detection of brain lesions with CNN, BB-MRI imaging represents a highly effective input type when compared to conventional CE T1-MRI imaging. Use of BB-MRI can overcome the current limitations for automated brain lesion detection and the objectively excellent performance of our CNN suggests routine usage of BB sequences for radiological analysis.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions
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