Fast filters for preprocessing and mass segmentation in mammography images

2021 ◽  
Author(s):  
Yuliana J. Gaona ◽  
Maria J. Rodríguez-Álvarez ◽  
Jimmy Freire ◽  
Darwin Castillo ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Mass Segmentation

scholarly journals Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

2021 ◽  
Author(s):  
Yutong Yan ◽  
Pierre-Henri Conze ◽  
Gwenolé Quellec ◽  
Mathieu Lamard ◽  
Beatrice Cochener ◽  
...  
Keyword(s):  
Breast Mass ◽  
Two Stage ◽  
Multi Scale ◽  
Mass Segmentation ◽  
User Intervention

Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms

2021 ◽  
Author(s):  
Wenwei Zhao ◽  
Meng Lou ◽  
Yunliang Qi ◽  
Yiming Wang ◽  
Chunbo Xu ◽  
...  
Keyword(s):  
Spatial Context ◽  
Breast Mass ◽  
Full Field ◽  
Mass Segmentation

scholarly journals Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network

2020 ◽  
Vol 61 ◽  
pp. 102027
Author(s):  
Michal Byra ◽  
Piotr Jarosik ◽  
Aleksandra Szubert ◽  
Michael Galperin ◽  
Haydee Ojeda-Fournier ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Breast Mass ◽  
Mass Segmentation

Mammographic Mass Segmentation Using Fuzzy C–means and Decision Trees

2018 ◽  
pp. 1-10 ◽  
Author(s):  
Damian Valdés-Santiago ◽  
Raúl Quintana-Martínez ◽  
Ángela León-Mecías ◽  
Marta Lourdes Baguer Díaz-Romañach
Keyword(s):  
Decision Trees ◽  
Fuzzy C Means ◽  
Mass Segmentation

scholarly journals Adversarial Deep Structural Networks for Mammographic Mass Segmentation

2016 ◽  
Author(s):  
Wentao Zhu ◽  
Xiaohui Xie
Keyword(s):  
Conditional Random Fields ◽  
Model Potential ◽  
Natural Image ◽  
Structural Learning ◽  
Mass Detection ◽  
Convolutional Network ◽  
Adversarial Training ◽  
Mass Segmentation ◽  
End To End ◽  
Public Datasets

AbstractMass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model potential function, followed by a CRF to perform structural learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with position priori for the task. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.

Breast Mass Segmentation in Mammographic Images by an Effective Region Growing Algorithm

2008 ◽  
pp. 948-957 ◽  
Author(s):  
Arianna Mencattini ◽  
Giulia Rabottino ◽  
Marcello Salmeri ◽  
Roberto Lojacono ◽  
Emanuele Colini
Keyword(s):  
Region Growing ◽  
Breast Mass ◽  
Mammographic Images ◽  
Mass Segmentation

Level Set Breast Mass Segmentation in Contrast-Enhanced and Non-Contrast-Enhanced Breast CT

2012 ◽  
pp. 697-704 ◽  
Author(s):  
Hsien-Chi Kuo ◽  
Maryellen L. Giger ◽  
Ingrid Reiser ◽  
John M. Boone ◽  
Karen K. Lindfors ◽  
...  
Keyword(s):  
Level Set ◽  
Breast Mass ◽  
Breast Ct ◽  
Contrast Enhanced ◽  
Mass Segmentation
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