Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China.
Foreign Language Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China.
J Healthc Eng. 2021 Oct 20;2021:1141619. doi: 10.1155/2021/1141619. eCollection 2021.
Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary method for breast tumor diagnosis. Although there is no universal perfect method for image segmentation so far, the consensus on the general law of image segmentation has produced considerable research results and methods. In this context, this paper focuses on the breast tumor image segmentation method based on CNN and proposes an improved DCNN method combined with CRF. This method can obtain the information of multiscale and pixels better. The experimental results show that, compared with DCNN without these methods, the segmentation accuracy is significantly improved.
早期诊断肿瘤对提高患者的治疗效果和生存率起着重要作用。然而，乳腺肿瘤的诊断很难通过有创检查来进行，因此医学影像已成为乳腺肿瘤诊断最直观的辅助方法。尽管到目前为止还没有通用的完美的图像分割方法，但对于图像分割的一般规律已经产生了相当多的研究成果和方法。在这种情况下，本文基于卷积神经网络（CNN）关注乳腺肿瘤图像分割方法，并提出了一种结合条件随机场（CRF）的改进的 DCNN 方法。该方法可以更好地获取多尺度和像素的信息。实验结果表明，与没有这些方法的 DCNN 相比，该分割方法的准确率有了显著提高。
