Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
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DOI：
10.1118/1.4967345
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发表时间：
2016-12-01
期刊：
影响因子：
3.8
通讯作者：
Cha, Kenny
中科院分区：
文献类型：
--
作者：
Samala, Ravi K.;Chan, Heang-Ping;Cha, Kenny
目的：开发一个计算机辅助检测（CAD）系统的质量在数字乳腺断层合成摄影（DBT）体积使用深度卷积神经网络（DCNN）从mammograms.Methods转移学习：数据集包含2282数字化的电影和数字乳腺X线照片和324 DBT体积收集IRB批准。由经验丰富的乳腺放射科医生标记图像上的感兴趣肿块作为参考标准。将数据集分为训练集（2282张乳房X线照片，2461个肿块和230张DBT视图，228个肿块）和独立测试集（94张DBT视图，89个肿块）。对于DCNN训练，从每个图像中提取包含质量（真阳性）的感兴趣区域（ROI）。假阳性（FP）ROI在预筛选时由其先前开发的CAD系统识别。数据增强后，共获得45 072个乳腺摄影ROI和37 450个DBT ROI。使用应用于每个ROI的背景校正方法实现了跨异质数据的ROI中的数据归一化和非均匀性的减少。首先在乳房X射线摄影数据上训练具有四个卷积层和三个全连接（FC）层的DCNN。使用抖动和丢弃技术来减少过拟合。在使用乳房X线摄影ROI进行训练后，前三个卷积层中的所有权重都被冻结，只有最后一个卷积层和FC层被再次随机初始化并使用DBT训练ROI进行训练。作者比较了两种CAD系统在DBT中用于质量检测的性能：一种使用基于DCNN的方法，另一种使用他们先前开发的基于特征的方法来减少FP。两个系统的预筛选阶段是相同的，将同一组质量候选物传递到FP减少阶段。对于基于特征的CAD系统，使用3D聚类和活动轮廓方法进行分割;提取形态、灰度和纹理特征，并与线性判别分类器合并以对检测到的肿块进行评分。对于基于DCNN的CAD系统，来自以每个候选者为中心的五个连续切片的ROI通过经训练的DCNN，并生成质量似然分数。CAD系统的性能进行了评估，使用自由响应ROC曲线和性能差异进行了分析，使用非参数的方法。结果：在迁移学习之前，DCNN训练只对乳房X线片与AUC为0.99分类DBT质量与AUC为0.81的DBT训练集。在使用DBT进行迁移学习后，AUC提高到0.90。对于测试集中基于乳房的CAD检测，在1 FP/DBT体积下，基于特征和基于DCNN的CAD系统的灵敏度分别为83%和91%。两种系统的性能之间的差异具有统计学意义（p值< 0.05）。结论：通过DCNN将从乳房X线照片中学习到的图像模式转移到DBT切片上的肿块检测中。这项研究表明，从乳腺X线摄影收集的大数据集对于开发用于DBT的新CAD系统是有用的，减轻了为新模式收集全新的大数据集的问题和努力。(C)2016年美国医学物理学家协会。
Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms.Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method.Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05).Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. (C) 2016 American Association of Physicists in Medicine.