Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks

Natalia Antropova; Benjamin Huynh; Hui Li; Maryellen L. Giger

doi:10.1117/1.JMI.6.1.011002

21 August 2018 Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks

Natalia Antropova, Benjamin Huynh, Hui Li, Maryellen L. Giger

Author Affiliations +

Journal of Medical Imaging, Vol. 6, Issue 1, 011002 (August 2018). https://doi.org/10.1117/1.JMI.6.1.011002

Abstract

We present a breast lesion classification methodology, based on four-dimensional (4-D) dynamic contrast-enhanced magnetic resonance images (DCE-MRI), using recurrent neural networks in combination with a pretrained convolutional neural network (CNN). The method enables to capture not only the two-dimensional image features but also the temporal enhancement patterns presented in DCE-MRI. We train a long short-term memory (LSTM) network on temporal sequences of feature vectors extracted from the dynamic MRI sequences. To capture the local changes in lesion enhancement, the feature vectors are obtained from various levels of a pretrained CNN. We compare the LSTM method’s performance to that of a CNN fine-tuned on “RGB” MRIs, formed by precontrast, first, and second postcontrast MRIs. LSTM significantly outperformed the fine-tuned CNN, resulting in AUC_LSTM = 0.88 and AUC_fine-tuned = 0.84, p = 0.00085, in the task of distinguishing benign and malignant lesions. Our method captures clinically useful information carried by the full 4-D dynamic MRI sequence and outperforms the standard fine-tuning method.

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Natalia Antropova, Benjamin Huynh, Hui Li, and Maryellen L. Giger "Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks," Journal of Medical Imaging 6(1), 011002 (21 August 2018). https://doi.org/10.1117/1.JMI.6.1.011002

Received: 10 April 2018; Accepted: 8 June 2018; Published: 21 August 2018

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