分割
前列腺癌
计算机科学
人工智能
深度学习
豪斯多夫距离
图像分割
模式识别(心理学)
GSM演进的增强数据速率
癌症
像素
计算机视觉
医学
内科学
作者
Zhixun Li,Jiancheng Fang,Ruiyun Qiu,Huiling Gong,Wei Zhang,Linghao Li,Jiang Jian
标识
DOI:10.1016/j.bspc.2023.104622
摘要
Prostate cancer is becoming one of the deadliest cancer in men, and the early diagnosis and detection of cancer can be effective in improving patient survival. Computer-aided diagnosis (CAD) has evolved rapidly in recent years, and more and more computer technologies are now being used for prostate cancer detection. Apparently, automatic and accurate segmentation is the most important step in it. However, the existing prostate cancer segmentation methods still have problems with low accuracy and efficiency, and these deficiencies prevent them from providing adequate results at the pixel level. In this paper, a prostate cancer segmentation network (CDA-Net) is proposed from the perspectives of the global cancer region localization and the local cancer edge recognition. Specifically, we propose a parallel dilated U-Net (dila-UNet) to extract deep features for more accurate localization, as well as design a connectivity mechanism of a generative adversarial network (GAN) and a contrastive learning module for finer edge recognition. Compared with some classic and state-of-the-art (SOTA) segmentation methods, the results show the segmentation performance of the proposed network is superiorly increased by ∼ 1.7%, ∼ 3.8% and ∼ 1.7% on IoU, PA and Dice, respectively, and the 95%hausdorff distance is decreased by ∼ 1.82, in MRI images.
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