Li Changsheng, Li Xue, Wang Kaifeng, Chen Wenxin, Liu Qingyao, Duan Xingguang
IEEE J Biomed Health Inform. 2024 Jul 29;PP. doi: 10.1109/JBHI.2024.3434372.
Endoscopy holds a pivotal role in the early detection and treatment of diverse diseases, with artificial intelligence (AI)-assisted methods increasingly gaining prominence in disease screening. Among them, the depth estimation from endoscopic sequences is crucial for a spectrum of AI-assisted surgical techniques. However, the development of endoscopic depth estimation algorithms presents a formidable challenge due to the unique environmental intricacies and constraints within the dataset. This paper proposes a self-supervised depth estimation network to comprehensively explore the brightness changes in endoscopic images, and fuse different features at multiple levels to achieve an accurate prediction of endoscopic depth. First, a FlowNet is designed to evaluate the brightness changes of adjacent frames by calculating the multi-scale structural similarity. Second, a feature fusion module is presented to capture multi-scale contextual information. Experiments show that the average accuracy of the algorithm is 97.03% in the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED dataset). Based on the training parameters of the SCARED dataset, the algorithm achieves superior performance on the other two datasets (EndoSLAM and KVASIR dataset), indicating that the algorithm has good generalization performance.
内镜检查在多种疾病的早期检测和治疗中起着关键作用，人工智能（AI）辅助方法在疾病筛查中越来越受到重视。其中，从内镜序列中进行深度估计对于一系列AI辅助手术技术至关重要。然而，由于数据集中独特的环境复杂性和约束条件，内镜深度估计算法的开发面临巨大挑战。本文提出了一种自监督深度估计网络，以全面探索内镜图像中的亮度变化，并在多个层次上融合不同特征，从而实现对内镜深度的准确预测。首先，设计了一个FlowNet，通过计算多尺度结构相似性来评估相邻帧的亮度变化。其次，提出了一个特征融合模块来捕获多尺度上下文信息。实验表明，该算法在《内镜数据的立体匹配与重建》（SCARED数据集）中的平均准确率为97.03%。基于SCARED数据集的训练参数，该算法在其他两个数据集（EndoSLAM和KVASIR数据集）上表现优异，表明该算法具有良好的泛化性能。
