Multi-modal Complete Breast Segmentation

2017 ◽  
pp. 519-527
Author(s):  
Hooshiar Zolfagharnasab ◽  
João P. Monteiro ◽  
João F. Teixeira ◽  
Filipa Borlinhas ◽  
Hélder P. Oliveira
Keyword(s):  
Breast Segmentation

Density Based Breast Segmentation for Mammograms Using Graph Cut and Seed Based Region Growing Techniques

2010 ◽  
Author(s):  
Nafiza Saidin ◽  
Umi Kalthum Ngah ◽  
Harsa Amylia Mat Sakim ◽  
Ding Nik Siong ◽  
Mok Kim Hoe ◽  
...  
Keyword(s):  
Region Growing ◽  
Graph Cut ◽  
Breast Segmentation

Breast segmentation in MRI: quantitative evaluation of three methods

2013 ◽  
Author(s):  
Albert Gubern-Mérida ◽  
Lei Wang ◽  
Michiel Kallenberg ◽  
Robert Martí ◽  
Horst K. Hahn ◽  
...  
Keyword(s):  
Quantitative Evaluation ◽  
Breast Segmentation

Automatic breast segmentation in digital mammography using a convolutional neural network

2020 ◽  
Author(s):  
Omid Haji Maghsoudi ◽  
Aimilia Gastounioti ◽  
Lauren Pantalone ◽  
Emily Conant ◽  
Despina Kontos
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Mammography ◽  
Breast Segmentation

scholarly journals Automated Breast Segmentation of Fat and Water MR Images Using Dynamic Programming

2015 ◽  
Vol 22 (2) ◽  
pp. 139-148 ◽  
Author(s):  
José A. Rosado-Toro ◽  
Tomoe Barr ◽  
Jean-Philippe Galons ◽  
Marilyn T. Marron ◽  
Alison Stopeck ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Mr Images ◽  
Breast Segmentation

scholarly journals Biomedical Image Segmentation and Analysis in Deep Learning

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Tuan Anh Tran ◽  
Tien Dung Cao ◽  
Vu-Khanh Tran ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Patient Treatment ◽  
Biomedical Data ◽  
Biomedical Image Processing ◽  
Human Organ ◽  
Biomedical Image ◽  
Breast Segmentation ◽  
Effective Diagnosis ◽  
Disease Analysis

Biomedical Image Processing, such as human organ segmentation and disease analysis, is a modern field in medicine development and patient treatment. Besides there are many kinds of image formats, the diversity and complexity of biomedical data is still a big issue to all of researchers in their applications. In order to deal with the problem, deep learning give us a successful and effective solutions. Unet and LSTM are two general approaches to the most of case of medical image data. While Unet helps to teach a machine in learning data from each image accompanied with its labelled information, LSTM helps to remember states from many slices of images by times. Unet gives us the segmentation of tumor, abnormal things from biomedical images and then the LSTM gives us the effective diagnosis on a patient disease. In this paper, we show some scenarios of using Unets and LSTM to segment and analysis on many kinds of human organ images and results of brain, retinal, skin, lung and breast segmentation.

scholarly journals Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI

2019 ◽  
Author(s):  
Lei Zhang ◽  
Aly A. Mohamed ◽  
Ruimei Chai ◽  
Bingjie Zheng ◽  
Shandong Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Mri ◽  
Learning Method ◽  
Diffusion Weighted ◽  
Breast Segmentation
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