The computer-aided diagnosis of prostate ultrasound images can aid in the detection and treatment of prostate cancer. However, the ultrasound images of the prostate sometimes come with serious speckle noise, low signal-to-noise ratio, and poor detection accuracy. To overcome this shortcoming, we proposed a deep learning model that integrates S-Mask R-CNN and Inception-v3 in the ultrasound image-aided diagnosis of prostate cancer in this paper. The improved S-Mask R-CNN was used to realize the accurate segmentation of prostate ultrasound images and generate candidate regions. The region of interest align algorithm was used to realize the pixel-level feature point positioning. The corresponding binary mask of prostate images was generated by the convolution network to segment the prostate region and the background. Then, the background information was shielded, and a data set of segmented ultrasound images of the prostate was constructed for the Inception-v3 network for lesion detection. A new network model was added to replace the original classification module, which is composed of forward and back propagation. Forward propagation mainly transfers the characteristics extracted from the convolution layer pooling layer below the pool_3 layer through the transfer learning strategy to the input layer and then calculates the loss value between the classified and label values to identify the ultrasound lesion of the prostate. The experimental results showed that the proposed method can accurately detect the ultrasound image of the prostate and segment prostate information at the pixel-level simultaneously. The proposed method has higher accuracy than that of the doctor’s manual diagnosis and other detection methods. Our simple and effective approach will serve as a solid baseline and help ease future research in the computer-aided diagnosis of prostate ultrasound images. Furthermore, this work will promote the development of prostate cancer ultrasound diagnostic technology.

前列腺超声图像的计算机辅助诊断可以帮助检测和治疗前列腺癌。但是，前列腺的超声图像有时会带有严重的斑点噪声，低信噪比和较差的检测精度。为了克服这一缺点，我们在本文中提出了一种将S-Mask R-CNN和Inception-v3集成在一起的深度学习模型，用于前列腺癌的超声图像辅助诊断。改进的S-Mask R-CNN用于实现前列腺超声图像的精确分割并生成候选区域。感兴趣区域对齐算法用于实现像素级特征点定位。卷积网络生成前列腺图像的相应二进制掩码，以分割前列腺区域和背景。然后，屏蔽背景信息，并为Inception-v3网络构建了前列腺分段超声图像数据集，用于病变检测。添加了新的网络模型来代替原始的分类模块，该模块由正向传播和反向传播组成。前向传播主要通过传递学习策略将从pool_3层以下的卷积层池化层提取的特征传递到输入层，然后计算分类值和标签值之间的损失值，以识别前列腺的超声损伤。实验结果表明，所提出的方法可以准确地检测出前列腺的超声图像，并同时在像素水平上分割前列腺信息。所提出的方法比医生的手动诊断和其他检测方法具有更高的准确性。我们简单有效的方法将成为坚实的基准，并有助于简化前列腺超声图像的计算机辅助诊断的未来研究。此外，这项工作将促进前列腺癌超声诊断技术的发展。