Optimal cropping for input images used in a convolutional neural network for ultrasonic diagnosis of liver tumors

In recent years there have been many studies on computer-aided diagnosis (CAD) using convolutional neural networks (CNNs). For CAD of a tumor, data are generally obtained by cropping a region of interest (ROI), including a tumor, in an image. However, ultrasonic diagnosis also uses information from around a tumor. Therefore, in CAD using ultrasound images, diagnostic accuracy could be improved by using a ROI that includes the periphery of the tumor. In this study, we examined how much of the surrounding area should be included in a ROI for a CNN using ultrasound images of liver tumors. We used the ratio between the maximum diameter of the tumor and the ROI size as the index for ROI cropping. Our results show that the diagnostic accuracy was maximized when this index is 0.6. Therefore, optimal ROI cropping is important in CNNs for ultrasonic diagnosis.

[1]  Hiroki Nishikawa,et al.  Usefulness of Attenuation Imaging with an Ultrasound Scanner for the Evaluation of Hepatic Steatosis. , 2019, Ultrasound in medicine & biology.

[2]  Guowu Yang,et al.  An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures , 2019, BMC Medical Imaging.

[3]  Ryo Nagaoka,et al.  Investigation of the estimation accuracy of two-step block matching methods using envelope and RF signals for two-dimensional blood flow vector imaging , 2019, Japanese Journal of Applied Physics.

[4]  Makoto Yamakawa,et al.  Evaluation of shear wave dispersion in hepatic viscoelastic models including fibrous structure , 2019, Japanese Journal of Applied Physics.

[5]  Ryo Nagaoka,et al.  Utilization of singular value decomposition in high-frame-rate cardiac blood flow imaging , 2019, Japanese Journal of Applied Physics.

[6]  Masahiro Yoshimoto,et al.  PEDOT:PSS/GaAs1−xBix organic–inorganic solar cells , 2019, Japanese Journal of Applied Physics.

[7]  J. Duncan,et al.  Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI , 2019, European Radiology.

[8]  Ukihide Tateishi,et al.  Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network , 2019, Japanese Journal of Radiology.

[9]  Makoto Yamakawa,et al.  Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology , 2019, Hepatology International.

[10]  Il Dong Yun,et al.  Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images , 2017, IEEE Transactions on Medical Imaging.

[11]  Kazuyuki Ishida,et al.  The B-Mode Image-Guided Ultrasound Attenuation Parameter Accurately Detects Hepatic Steatosis in Chronic Liver Disease. , 2018, Ultrasound in medicine & biology.

[12]  Reyer Zwiggelaar,et al.  Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks , 2018, IEEE Journal of Biomedical and Health Informatics.

[13]  Hayato Itoh,et al.  Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. , 2018, Gastroenterology.

[14]  Yonina C. Eldar,et al.  Sparse Convolutional Beamforming for Ultrasound Imaging , 2018, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[15]  Hayit Greenspan,et al.  GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.

[16]  M. Fujishiro,et al.  Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images , 2018, Gastric Cancer.

[17]  Brett Byram,et al.  Deep Neural Networks for Ultrasound Beamforming , 2018, IEEE Transactions on Medical Imaging.

[18]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[19]  Paul Babyn,et al.  Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network , 2017, Journal of Digital Imaging.

[20]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[21]  Piero Tortoli,et al.  High Frame-Rate, High Resolution Ultrasound Imaging With Multi-Line Transmission and Filtered-Delay Multiply And Sum Beamforming , 2017, IEEE Trans. Medical Imaging.

[22]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[23]  M. Tanter,et al.  Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging , 2015, Nature.

[24]  Tsuyoshi Shiina,et al.  WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 1: basic principles and terminology. , 2015, Ultrasound in medicine & biology.

[25]  Naohisa Kamiyama,et al.  Real-time ultrasound attenuation imaging of diffuse fatty liver disease. , 2013, Ultrasound in medicine & biology.

[26]  A. Austeng,et al.  An approach to multibeam covariance matrices for adaptive beamforming in ultrasonography , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[27]  J Bercoff,et al.  Ultrafast compound doppler imaging: providing full blood flow characterization , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[28]  C.-I.C. Nilsen,et al.  Beamspace adaptive beamforming for ultrasound imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[29]  A. Austeng,et al.  Adaptive Beamforming Applied to Medical Ultrasound Imaging , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[30]  T. Matsumura,et al.  Breast disease: clinical application of US elastography for diagnosis. , 2006, Radiology.

[31]  M. Fink,et al.  Supersonic shear imaging: a new technique for soft tissue elasticity mapping , 2004, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[32]  T. Matsumura,et al.  High-speed Freehand Tissue Elasticity Imaging for Breast Diagnosis , 2003 .

[33]  Kouichi Itoh,et al.  A New Method for Attenuation Coefficient Measurement in the Liver , 2002, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[34]  J A Zagzebski,et al.  Ultrasound backscatter and attenuation in human liver with diffuse disease. , 1999, Ultrasound in medicine & biology.

[35]  J. Ophir,et al.  Elastography: A Quantitative Method for Imaging the Elasticity of Biological Tissues , 1991, Ultrasonic imaging.