Li Yanfeng, Zhang Linlin, Chen Houjin, Cheng Lin
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
Comput Methods Programs Biomed. 2020 Oct;195:105518. doi: 10.1016/j.cmpb.2020.105518. Epub 2020 May 24.
Automatic detection of the masses in mammograms is a big challenge and plays a crucial role to assist radiologists for accurate diagnosis. In this paper, a bilateral image analysis method based on Convolution Neural Network (CNN) is developed for mass detection in mammograms.
The proposed bilateral mass detection method consists of two networks: a registration network for registering bilateral mammograms and a Siamese-Faster-RCNN network for mass detection using a pair of registered mammograms. In the first step, self-supervised learning network is built to learn the spatial transformation between bilateral mammograms. This network can directly estimate spatial transformation by maximizing an image-wise similarity metric and corresponding points labeling is not needed. In the second step, an end-to-end network combining the Region Proposal Network (RPN) and a Siamese Fully Connected (Siamese-FC) network is designed. Different from existing methods, the designed network integrates mass detection on single image with registered bilateral images comparison.
The proposed method is evaluated on three datasets (publicly available dataset INbreast and private dataset BCPKUPH and TXMD). For INbreast dataset, the proposed method achieves 0.88 true positive rate (TPR) with 1.12 false positives per image (FPs/I). For BCPKUPH dataset, the proposed method achieves 0.85 TPR with 1.86 FPs/I. For TXMD dataset, the proposed method achieves 0.85 TPR with 2.70 FPs/I.
Registration experimental result shows that the proposed method is suitable for bilateral mass detection. Mass detection experimental results show that the proposed method performs better than unilateral mass detection method, different bilateral connection schemes and image level fusion bilateral schemes.
乳腺钼靶片中肿块的自动检测是一项重大挑战，对辅助放射科医生进行准确诊断起着至关重要的作用。本文提出了一种基于卷积神经网络（CNN）的双边图像分析方法，用于乳腺钼靶片中的肿块检测。
所提出的双边肿块检测方法由两个网络组成：一个用于配准双边乳腺钼靶片的配准网络，以及一个使用一对配准后的乳腺钼靶片进行肿块检测的连体快速区域卷积神经网络（Siamese-Faster-RCNN）。第一步，构建自监督学习网络来学习双边乳腺钼靶片之间的空间变换。该网络可以通过最大化图像级相似性度量直接估计空间变换，无需对应点标注。第二步，设计一个结合区域提议网络（RPN）和连体全连接（Siamese-FC）网络的端到端网络。与现有方法不同，所设计的网络将单图像上的肿块检测与配准后的双边图像比较相结合。
所提出的方法在三个数据集（公开可用的INbreast数据集以及私有数据集BCPKUPH和TXMD）上进行了评估。对于INbreast数据集，所提出的方法实现了0.88的真阳性率（TPR），每张图像的假阳性数为1.12（FPs/I）。对于BCPKUPH数据集，所提出的方法实现了0.85的TPR，每张图像的假阳性数为1.86（FPs/I）。对于TXMD数据集，所提出的方法实现了0.85的TPR，每张图像的假阳性数为2.70（FPs/I）。
配准实验结果表明所提出的方法适用于双边肿块检测。肿块检测实验结果表明，所提出的方法比单边肿块检测方法、不同的双边连接方案和图像级融合双边方案表现更好。
