Zhao Liang, Jia Chaoran, Ma Jiajun, Shao Yu, Liu Zhuo, Yuan Hong
School of Software Technology, Dalian University of Technology, Dalian, China.
The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Front Oncol. 2023 Apr 14;13:1109786. doi: 10.3389/fonc.2023.1109786. eCollection 2023.
Automatic segmentation of medical images has been a hot research topic in the field of deep learning in recent years, and achieving accurate segmentation of medical images is conducive to breakthroughs in disease diagnosis, monitoring, and treatment. In medicine, MRI imaging technology is often used to image brain tumors, and further judgment of the tumor area needs to be combined with expert analysis. If the diagnosis can be carried out by computer-aided methods, the efficiency and accuracy will be effectively improved. Therefore, this paper completes the task of brain tumor segmentation by building a self-supervised deep learning network. Specifically, it designs a multi-modal encoder-decoder network based on the extension of the residual network. Aiming at the problem of multi-modal feature extraction, the network introduces a multi-modal hybrid fusion module to fully extract the unique features of each modality and reduce the complexity of the whole framework. In addition, to better learn multi-modal complementary features and improve the robustness of the model, a pretext task to complete the masked area is set, to realize the self-supervised learning of the network. Thus, it can effectively improve the encoder's ability to extract multi-modal features and enhance the noise immunity. Experimental results present that our method is superior to the compared methods on the tested datasets.
近年来，医学图像的自动分割一直是深度学习领域的研究热点，实现医学图像的精确分割有助于在疾病诊断、监测和治疗方面取得突破。在医学中，磁共振成像（MRI）技术常用于脑部肿瘤成像，而肿瘤区域的进一步判断需要结合专家分析。如果能通过计算机辅助方法进行诊断，将有效提高效率和准确性。因此，本文通过构建自监督深度学习网络完成脑肿瘤分割任务。具体而言，基于残差网络的扩展设计了一个多模态编码器 - 解码器网络。针对多模态特征提取问题，该网络引入了一个多模态混合融合模块，以充分提取各模态的独特特征并降低整个框架的复杂度。此外，为了更好地学习多模态互补特征并提高模型的鲁棒性，设置了一个完成掩码区域的前置任务，以实现网络的自监督学习。从而能够有效提高编码器提取多模态特征的能力并增强抗噪性。实验结果表明，我们的方法在测试数据集上优于比较方法。
