27 February 2018 Stability of deep features across CT scanners and field of view using a physical phantom
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Radiomics is the process of analyzing radiological images by extracting quantitative features for monitoring and diagnosis of various cancers. Analyzing images acquired from different medical centers is confounded by many choices in acquisition, reconstruction parameters and differences among device manufacturers. Consequently, scanning the same patient or phantom using various acquisition/reconstruction parameters as well as different scanners may result in different feature values. To further evaluate this issue, in this study, CT images from a physical radiomic phantom were used. Recent studies showed that some quantitative features were dependent on voxel size and that this dependency could be reduced or removed by the appropriate normalization factor. Deep features extracted from a convolutional neural network, may also provide additional features for image analysis. Using a transfer learning approach, we obtained deep features from three convolutional neural networks pre-trained on color camera images. An we examination of the dependency of deep features on image pixel size was done. We found that some deep features were pixel size dependent, and to remove this dependency we proposed two effective normalization approaches. For analyzing the effects of normalization, a threshold has been used based on the calculated standard deviation and average distance from a best fit horizontal line among the features’ underlying pixel size before and after normalization. The inter and intra scanner dependency of deep features has also been evaluated.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rahul Paul, Rahul Paul, Muhammad Shafiq-ul-Hassan, Muhammad Shafiq-ul-Hassan, Eduardo G. Moros, Eduardo G. Moros, Robert J. Gillies, Robert J. Gillies, Lawrence O. Hall, Lawrence O. Hall, Dmitry B. Goldgof, Dmitry B. Goldgof, } "Stability of deep features across CT scanners and field of view using a physical phantom ", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753P (27 February 2018); doi: 10.1117/12.2293164; https://doi.org/10.1117/12.2293164

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