Abstract
Automatic mass detection in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) helps to reduce the workload of radiologists and improves diagnostic accuracy. However, most of the existing methods rely on hand-crafted features followed by rule-based or shallow machine learning based detection methods. Due to the limited expressive power of hand-crafted features, the diagnostic performances of existing methods are usually unsatisfactory. In this work, we aim to leverage recent deep learning techniques for breast lesion detection and propose the Spatiotemporal Breast Mass Detection Networks (MD-Nets) to detect the masses in the 4D DCE-MRI images automatically. Simulating the clinical diagnosis process, we initially generate image-based candidates from all individual images and then construct a spatiotemporal 4D data to classify mass by using the convolutional long short-term memory network (ConvLSTM) to incorporate kinetic and spatial characteristics. Moreover, we collect a DCE-MRI dataset containing 21,294 annotated images from 172 studies. In experiments, we achieve an AUC of 0.9163 with a sensitivity of 0.8655 and a specificity of 0.8452, which verifies the effectiveness of our method.
This work was supported by the National Key Research and Development Program of China (2017YFB1002202), and the National Natural Science Foundation of China (61871004, 61572472, 61525206).
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Deng, L., Tang, S., Fu, H., Wang, B., Zhang, Y. (2019). Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D DCE-MRI Images. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_30
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DOI: https://doi.org/10.1007/978-3-030-32251-9_30
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