Automatic breast segmentation in digital mammography using a convolutional neural network

机译：使用卷积神经网络在乳腺X线摄影中自动进行乳房分割

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Digital mammography (DM) has boon considered as the primary modality for breast rancer screening. The relative amount of breast fibroglandular tissue, referred to as percent breast density (PD). has been considered as an important factor associated with breast cancer. We have developed and tested a robust method to accurately segment the pectoral muscle and the breast area using a deep learning approach. We use a U-Net architecture with a ResNet decoder to increase the depth of features. The architecture is trained using 555 DM images and tested and validated on an independent set, of 555 images. The results show that our network achieves an average and standard deviation dice coefficient of 94.86% ± 1.93%. respectively, and sensitivity of 96.31% ± 1.87%. The method present here can be considered as the first step toward the automatic estimation of PD.

机译：数字乳腺X线摄影术（DM）已被认为是乳腺癌筛查的主要方式。乳腺纤维腺组织的相对量，称为乳腺密度百分比（PD）。已被认为是与乳腺癌相关的重要因素。我们已经开发并测试了一种健壮的方法，可以使用深度学习方法来准确地分割胸肌和乳房区域。我们使用带有ResNet解码器的U-Net架构来增加功能的深度。使用555 DM图像对体系结构进行了培训，并在独立的555图像集上进行了测试和验证。结果表明，我们的网络实现了94.86％±1.93％的平均和标准偏差骰子系数。灵敏度分别为96.31％±1.87％。这里存在的方法可以被认为是自动进行PD评估的第一步。

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《International Workshop on Breast Imaging》|2020年|1151322.1-1151322.5|共6页
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Omid Haji Maghsoudi; Aimilia Gastounioti; Lauren Pantalone; Emily Conant; Despina Kontos;
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Digital Mammography; Deep Learning; Segmentation; Breast Cancer;

机译：数字化乳腺摄影;深度学习;分割;乳腺癌;

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1. Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography [J] . Mendel Kayla, Li Hui, Sheth Deepa, Academic radiology . 2019,第6期

机译：从卷积神经网络转移学习，用于计算机辅助诊断：数字乳房断层合成和全场数字乳房X线摄影的比较
2. Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net [J] . Zhang Yang, Chen Jeon-Hor, Chang Kai-Ting, Academic radiology . 2019,第11期

机译：乳房MRI中的自动乳房和纤维群组织分割，通过完全卷积的残余神经网络U-Net使用深度学习
3. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI [J] . Han Jiao, Xinhua Jiang, Zhiyong Pang, Computational and mathematical methods in medicine . 2020,第1期

机译：基于深度卷积神经网络的自动乳房分割和DCE-MRI的质量检测
4. Evaluation of a Deep Convolutional Neural Network method for the segmentation of breast microcalcifications in Mammography Imaging [C] . G. Valvano, D. Delia Latta, N. Martini European Medical Biological Engineering Conference . 2018

机译：评价乳房X线摄影成像中乳房微钙化分割的深度卷积神经网络方法
5. Automated Segmentation and Uncertainty Quantification for Image-Based Cardiovascular Modeling with Convolutional Neural Networks [D] . Maher, Gabriel. 2020

机译：卷积神经网络的图像基础心血管建模的自动分割和不确定量化
6. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI [O] . Han Jiao, Xinhua Jiang, Zhiyong Pang, 2020

机译：DCE-MRI中基于深度卷积神经网络的乳房自动分割和质量检测
7. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography [O] . Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, 2016

机译：数字乳房断层合成中的大规模检测：深度卷积神经网络，从乳房X线摄影学习

Automatic breast segmentation in digital mammography using a convolutional neural network

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