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DCE-MRI interpolation using learned transformations for breast lesions classification

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Abstract

Automatic differentiation of benign and malignant breast lesions on multiple DCE-MRI series is a challenging task. The performance of the Convolutional Neural Networks (CNNs) based methods is severely affected when the number of DCE-MRI series is inadequate or inconsistent. This paper is motivated by the need of capturing spatial-temporal features from consistent DCE-MRI series for most CNN-based classification methods, and aims at designing an interpolation network that can enlarge the DCE-MRI series. Therefore, our method achieves the objective of breast lesion classification for inconsistent DCE-MRI series with a two-stage method, i.e., DCE-MRI interpolation and classification. Inspired by the learning-based data augmentation, we propose a variable-length multiple DCE-MRI series interpolation method using learned transformations to enlarge DCE-MRI series. Specifically, the forward and backward contrast transformations are learned to estimate the kinetic and spatial variation between different DCE-MRI series. Then, an adaptive warping method is proposed to generate multiple interpolated DCE-MRI series. Finally, the spatial-temporal features are extracted by a new two-stream network from the interpolated DCE-MRI and they are further used to classify breast lesions. We justify the proposed method through extensive experiments using 1223 DCE-MRI slices. Comparing to other methods, it achieves better results on both single series interpolation and multiple series interpolation. The interpolated DCE-MRI greatly improves the classification accuracy nearly by 5% and the best accuracy is 81.9%.

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Acknowledgements

This research was supported by the Youth Program of National Natural Science Foundation of China (No. 62001380); the Key Research and Development Program of Shaanxi Province (the General Project of Social Development) (2020SF-049); Scientific Research Project of Education Department of Shaanxi Provincial Government (19JK0808); Xi’an Science and Technology Plan Project(20YXYJ0010(5)).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, 710121, China

    Hongyu Wang, Cong Gao & Xiaoying Pan

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, 710121, China

    Hongyu Wang, Cong Gao & Xiaoying Pan

  3. Department of Information Science and Technology, Northwest University, Xi’an, Shaanxi, 7101127, China

    Jun Feng

  4. Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, 710038, China

    Di Yang

  5. Imaging Diagnosis and Treatment Center, Xi’an International Medical Center Hospital, Xi’an, Shaanxi, 710110, China

    Baoying Chen

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  1. Hongyu Wang
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Correspondence to Hongyu Wang.

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Wang, H., Gao, C., Feng, J. et al. DCE-MRI interpolation using learned transformations for breast lesions classification. Multimed Tools Appl 80, 26237–26254 (2021). https://doi.org/10.1007/s11042-021-10919-8

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