A two-stage CNN method for MRI image segmentation of prostate with lesion. (April 2023)

• Record Type: Journal Article
• Authors: Wang, Zixuan; Wu, Ruofan; Xu, Yanran; Liu, Yi; Chai, Ruimei; Ma, He
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: Prostate MRI images -- Medical image segmentation -- Convolutional neural networks -- Squeeze-excitation module -- Residual-attention U-net; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104610
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Prostate magnetic resonance imaging (MRI) is widely used in the diagnosis of prostate cancer and other prostate diseases. The automatic segmentation of images from prostate MRI plays an important role in the auxiliary diagnosis of prostate diseases. Currently, there are two commonly used methods for automatic segmentation of prostate MRI, which are 2D image segmentation and 3D image segmentation. In this paper, a two-stage CNN method for MRI image segmentation of prostate with lesion is proposed. At the first stage, we used a CNN model incorporating the Squeeze-Excitation module to discriminate whether the image contains prostate or not. At the second stage, we proposed a Residual-Attention U-Net for segmentation of images containing prostate. Eventually, the 3D prostate MRI segmentation results are obtained and fully automated segmentation is accomplished. We evaluated our proposed method and other common 2D and 3D segmentation methods on the test dataset and compared their results based on Dice Similarity Coefficient (DSC) value. Our method performed the best and achieved the DSC metric value of 0.860. Highlights: Highly automated segmentation method. Combination of classification and segmentation models based on neural networks. Improve the segmentation accuracy of MRI images of prostate cancer patients. The use of two different kinds of Attention Mechanisms.