Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks

Artificial Intelligence in Medicine
Gabriele PiantadosiCarlo Sansone

Abstract

Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual DCE-MRI examination can be difficult and error-prone. The early stage of breast tissue segmentation, in a typical CAD, is crucial to increase reliability and reduce the computational effort by reducing the number of voxels to analyze and removing foreign tissues and air. In recent years, the deep convolutional neural networks (CNNs) enabled a sensible improvement in many visual tasks automation, such as image classification and object recognition. These advances also involved radiomics, enabling high-throughput extraction of quantitative features, resulting in a strong improvement in automatic diagnosis through medical imaging. However, machine learning and, in particular, deep learning approaches are gaining popularity in the radiomics field for tissue segmentation. This work aims to accurately segment breast parenchyma from the air and other tissues (such as chest-wall) by applying an ensemble of deep CNNs on 3D MR data. The novelty, besides applying cutting-edge techniques in th...Continue Reading

Citations

Sep 17, 2020·Contrast Media & Molecular Imaging·Anke Meyer-BäseKatja Pinker
Sep 24, 2020·Cancers·Roberta FuscoAntonella Petrillo
May 6, 2021·Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)·Michele AvanzoGiovanni Mettivier
Jul 30, 2021·Medical Image Analysis·Vaanathi SundaresanLudovica Griffanti
Aug 21, 2021·Artificial Intelligence in Medicine·Yang HuWendy Hall

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