目的 乳腺癌是常见的高发病率肿瘤疾病，早期确诊是预防乳腺癌的关键。为获得肿瘤准确的边缘和形状信息，提高乳腺肿瘤诊断的准确性，本文提出了一种结合残差路径及密集连接的乳腺超声肿瘤分割方法。方法 基于经典的深度学习分割模型U-Net，添加残差路径，减少编码器和解码器特征映射之间的差异。在此基础上，在特征输入层到解码器最后一步之间引入密集块，通过密集块组成从输入特征映射到解码最后一层的新连接，减少输入特征图与解码特征图之间的差距，减少特征损失并保存更有效信息。结果 将本文模型与经典的U-Net模型、引入残差路径的U-Net （U-Net with Res paths）模型在上海新华医院崇明分院乳腺肿瘤超声数据集上进行10-fold交叉验证实验。本文模型的真阳率（true positive，TP）、杰卡德相似系数（Jaccard similarity，JS）和骰子系数（Dice coefficients，DC）分别为0.870 7、0.803 7和0.882 4，相比U-Net模型分别提高了1.08%、2.14%和2.01%；假阳率（false positive，FP）和豪斯多夫距离（Hausdorff distance，HD）分别为0.104 0和22.311 4，相比U-Net模型分别下降了1.68%和1.410 2。在54幅图像的测试集中，评价指标JS > 0.75的肿瘤图像数量的总平均数为42.1，最大值为46。对比实验结果表明，提出的算法有效改善了分割结果，提高了分割的准确性。结论 本文提出的基于U-Net结构并结合残差路径与新的连接的分割模型，改善了乳腺超声肿瘤图像分割的精确度。

Objective Precise segmentation of breast cancer tumors is of great concern. For women, breast cancer is a common tumor disease with a high incidence, and obtaining accurate diagnosis in the early stage of breast cancer has always been the key to preventing breast cancer. Doctors can improve the accuracy of the diagnosis of breast tumors by obtaining accurate information on the edge and shape of the tumor. Common breast imaging techniques include ultrasound imaging, magnetic resonance imaging (MRI), and X-ray imaging. However, X-ray imaging often causes radiation damage to breast tissue in women, whereas MRI imaging is not only expensive but also needs a longer scanning time. Compared with the two methods above, the ultrasound imaging detection method has the advantages of no radiation damage to tissue, ease of use, imaging the front of any breast, fast imaging speed, and cheap price. However, ultrasound images rely more on professional ultrasound doctors because of problems such as speckle noise and low resolution than other commonly used techniques. Thus, experienced, well-trained doctors are needed in the diagnostic process. In recent years, improving the accuracy of diagnosis by combining medical imaging technology with computer science and technology to segment tumors accurately and help related medical personnel in diagnosis and identification has become a trend. In the past 10 years, various methods, such as thresholding method, clustering-based algorithm, graph-based algorithm, and active contour algorithm, have been used to segment breast tumors on ultrasound images. However, these methods have limited ability to represent features. In the past few years, deep convolutional neural networks have become more widely used in visual recognition tasks. They can automatically find suitable features for target data and tasks. The convolutional network has existed for a long time. However, the hardware environment at that time limited its development because the size of the training set and the size of the network structure parameters require a large amount of computation. Fully convolutional network (FCN) is an effective convolutional neural network for semantic segmentation. It can be trained in an end-to-end and pixel-to-pixel manner. Its input image size is arbitrary, and the output image is a picture with its corresponding size, containing the target information. U-Net is an improvement of the FCN model. It not only solves the above problems but also can make full use of sample image to train a biological medical image well. Method In this paper, a deep learning segmentation model is proposed based on the U-Net framework, combining the "Res paths" to reduce the difference between the encoder and decoder feature maps, and establish a new connection composed of dense units. The "Res paths" consist of a series of residual units, which are composed of a 3×3 convolution kernel and a 1×1 convolution kernel. The number of residual units is 4, 3, 2, and 1 in order, set along four "residual paths (Res paths)" in the framework. The new connection is a dense block from the input of feature maps to the decoding part, and the input of each layer concatenated by the output of each previous layer alleviates the loss of feature information and the disappearance of gradient. The dataset from Chongming branch of Xinhua Hospital in Shanghai is applied in this paper. The dataset is obtained by Samsung RS80A color Doppler ultrasound diagnostic instrument (equipped with a high-frequency probe l3-12a). These images obtained from the instrument clearly show the morphology, internal structure, and surrounding tissues of the lesion. All patients from this dataset are female, aged from 24 to 86, in non-pregnancy and lactation, and have no history of radiotherapy, chemotherapy, or endocrine therapy before the examination. Ten-fold cross validation is used, and 538 breast ultrasound tumor images selected from the dataset are randomly divided into 10 cases. In one case, 54 breast ultrasound images are tested, and the 484 remaining pictures are used for training. In the experiment, 484 images are doubled to 968 images by image augmentation with image data generator. During training, 48 pictures of breast cancer tumors are randomly selected for validation. Keras is used to build the model framework. Training the model is started on NVIDIA Titan 1080 GPU utilizing the weights "he_normal" to initialize the parameters of the model. Our proposed model is trained by employing the Adam optimizer, using cross entropy as the loss function, and setting batch size, β1, β2, and learning rate to 4, 0.9, 0.999, and 0.000 1, respectively. Result The three models are cross-checked 10 times (U-Net, U-Net with Res, and the proposed model) using the same test sample sets, validation samples sets, and training sample sets each time. The first model is the classic U-Net model. The second model adds "residual paths" to the basic network structure of U-Net. The third method, proposed by us, is an improvement on the second method. Based on the second method, a new connection is introduced. The epochs of the three previous models are 80, 100, and 120 in order. Compared with the classic U-Net model, the true positive, Jaccard similarity (JS), and Dice coefficients of the proposed model are 0.870 7, 0.803 7, and 0.882 4, respectively, improving by 1.08%, 2.14%, and 2.01%, respectively. The indices of false positive and Hausdorff distance are 0.104 and 22.311 4, respectively, decreasing by 1.68% and 1.410 2, respectively. In the test set of every 54 pictures, the total average number of tumor pictures of JS > 0.75 is 42.1 up to a maximum of 46. Experimental results show that the proposed improved algorithm improves the results. Conclusion The proposed segmentation model based on U-Net network and combining the residual path with the new junction improves the precision of segmentation of breast ultrasound tumor images.