Health Management Center, Second Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
Nuclear Medicine Department, Second Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
J Healthc Eng. 2022 Jan 31;2022:7247549. doi: 10.1155/2022/7247549. eCollection 2022.
In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence nuclear medicine automated images in tumor diagnosis. This paper studies the methods to improve the accuracy of the segmentation algorithm from the perspective of boundary recognition and shape changeable adaptive capabilities, studies the active contour model based on boundary constraints, and proposes a superpixel boundary-aware convolution network to realize the automatic CT cutting algorithm. In this way, the tumor image can be cut more accurately. The experimental results in this paper show that the improved algorithm in this paper is more robust than the traditional CT algorithm in terms of accuracy and sensitivity, an increase of about 12%, and a slight increase in the negative prediction rate of 3%. In the comparison of cutting images of malignant tumors, the cutting effect of the algorithm in this paper is about 34% higher than that of the traditional algorithm.
为了正确获取正常组织和器官以及肿瘤病变，基于深度学习的多模态医学图像分割的研究对于充分实现自动分割算法更有意义。本文旨在研究基于人工智能的核医学自动图像在肿瘤诊断中的应用。本文从边界识别和形状可变形自适应能力的角度研究了提高分割算法精度的方法，研究了基于边界约束的主动轮廓模型，并提出了一种超像素边界感知卷积网络，以实现自动 CT 切割算法。这样，肿瘤图像可以更准确地进行切割。本文的实验结果表明，与传统 CT 算法相比，本文改进的算法在准确性和灵敏度方面更具稳健性，提高了约 12%，阴性预测率仅略有上升，为 3%。在恶性肿瘤的切割图像比较中，本文算法的切割效果比传统算法高出约 34%。
