Classification for Rectal CEUS Images Based on Combining Features by Transfer Learning

It is very important to diagnose patients with rectal cancer, which can provide reference for the follow-up treatment. The gold standard for rectal cancer diagnosis is biopsy, but biopsy is invasive and risky. With the development of contrast-enhanced ultrasound (CEUS) technology, CEUS has become a reliable modality to diagnose rectal cancer. The degree of contrast enhancement can reflect the distribution of micro vessels inside the tumor. CEUS images are classified into three grades according to the inhomogeneity of enhancement inside rectal cancer. In this paper, we use deep learning and transfer learning to classify CEUS images. Features of rectal CEUS images were extracted by AlexNet, VGG16 and Resnet50. The extracted features were combined and normalized. A three-layer fully connected neural network was trained to classify the features of rectal CEUS images. The combination of features extracted by VGG16 and ResNet50 achieve 87.91% accuracy and AUC is 0.978.

[1]  Alan D. Lopez,et al.  Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study , 2017, JAMA oncology.

[2]  Zhi-gang Yang,et al.  Features of time-intensity curve parameters of colorectal adenocarcinomas evaluated by double-contrast enhanced ultrasonography: initial observation. , 2012, European journal of radiology.

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Natalia Antropova,et al.  A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets , 2017, Medical physics.

[5]  J. Habbema,et al.  Changing role of 3 screening modalities in the European randomized study of screening for prostate cancer (Rotterdam) , 1999, International journal of cancer.

[6]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[7]  Mingyue Ding,et al.  Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound , 2014 .

[8]  Bin Song,et al.  Double–Contrast‐Enhanced Sonography for Diagnosis of Rectal Lesions With Pathologic Correlation , 2014, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[9]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[10]  Christopher V. Barback,et al.  Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics. , 2012, Journal of vacuum science and technology. B, Nanotechnology & microelectronics : materials, processing, measurement, & phenomena : JVST B.

[11]  Madhu S. Nair,et al.  Computer-aided grading of prostate cancer from MRI images using Convolutional Neural Networks , 2019, J. Intell. Fuzzy Syst..

[12]  Ilias Gatos,et al.  A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound. , 2015, Medical physics.

[13]  M. Bosio,et al.  Guidelines and Good Clinical Practice Recommendations for Contrast Enhanced Ultrasound (CEUS) - Update 2008 , 2008, Ultraschall in der Medizin.

[14]  Emilio Quaia,et al.  Characterization of focal liver lesions with contrast-specific US modes and a sulfur hexafluoride-filled microbubble contrast agent: diagnostic performance and confidence. , 2004, Radiology.

[15]  C. Dietrich,et al.  Comments and Illustrations Regarding the Guidelines and Good Clinical Practice Recommendations for Contrast-Enhanced Ultrasound (CEUS) – Update 2008 , 2008, Ultraschall in der Medizin.

[16]  Petia Radeva,et al.  A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound , 2017, IEEE Journal of Biomedical and Health Informatics.

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).