Abstract
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is a popular tool for the diagnosis of breast lesions due to its effectiveness, especially in a high risk population. Accurate lesion segmentation is an important step for subsequent analysis, especially for computer aided diagnosis systems. However, manual breast lesion segmentation of (4D) MRI is time consuming, requires experience, and it is prone to interobserver and intraobserver variability. This work proposes a deep learning (DL) framework for segmenting breast lesions in DCE-MRI using a 3D patch based U-Net architecture. We perform different experiments to analyse the effects of class imbalance, different patch sizes, optimizers and loss functions in a cross-validation fashion using 46 images from a subset of a challenging and publicly available dataset not reported to date, that is the TCGA-BRCA. We also compare the proposed U-Net framework with another state-of-the-art approach used for breast lesion segmentation in DCE-MRI, and report better segmentation accuracy with the proposed framework. The results presented in this work have the potential to become a publicly available benchmark for this task.
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Acknowledgments
This work was partially supported by the project ICEBERG: Image Computing for Enhancing Breast Cancer Radiomics (RTI2018-096333-B-I00, Spanish Ministry). We would also like to thank the TCGA Breast Phenotype Research Group for providing the computer-extracted lesion segmentation data used in this study, which comes from the University of Chicago lab of Maryellen Giger.
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Khaled, R., Vidal, J., Martí, R. (2021). Deep Learning Based Segmentation of Breast Lesions in DCE-MRI. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_32
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