Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.

中文翻译：

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DCE-MRI 中基于深度卷积神经网络的自动乳房分割和肿块检测。


医学图像中的乳房分割和肿块检测对于诊断和治疗随访非常重要。这些具有挑战性的任务的自动化可以帮助放射科医生减少乳腺癌分析的高人工工作量。在本文中，深度卷积神经网络（DCNN）被用于动态对比增强磁共振成像（DCE-MRI）中的乳房分割和肿块检测。首先，通过构建基于 U-Net++ 的全卷积神经网络，将乳房区域从其余身体部位中分割出来。利用深度学习的方法提取目标区域，有助于减少乳房外界的干扰。其次，使用卷积神经网络（Faster RCNN）的更快区域对分割的乳房图像进行质量检测。本研究使用的DCE-MRI数据集来自75例患者，采用5倍交叉验证方法。通过计算Dice相似系数(DSC)、Jaccard系数和分割灵敏度对乳房区域分割进行统计分析。为了验证乳腺肿块检测，计算并分析了每个病例的假阳性数量的敏感性。乳腺区域分割的Dice和Jaccard系数以及分割灵敏度值分别为0.951、0.908和0.948，优于原始U-Net算法，质量检测的平均灵敏度达到0.874，误报3.4个每个案例。