Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics

Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging problem and an active area of research. Particular challenges, similarly as in other segmentation problems, include the class-imbalance problem as well as confounding background in DCE-...

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Veröffentlicht in:	IEEE transactions on medical imaging 2019-02, Vol.38 (2), p.435-447
Hauptverfasser:	Zhang, Jun, Saha, Ashirbani, Zhu, Zhe, Mazurowski, Maciej A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Biopsy Breast Breast cancer Breast Neoplasms - classification Breast Neoplasms - diagnostic imaging Breast Neoplasms - genetics breast tumor Breast tumors Dynamic contrast-enhanced magnetic resonance imaging Feature extraction Female Genomics Humans Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Lesions Magnetic resonance imaging Magnetic Resonance Imaging - methods Masks Medical imaging molecular subtype classification Neural networks Neural Networks, Computer Nipples NMR Nuclear magnetic resonance segmentation Three dimensional models Tumors
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