Structure attention co-training neural network for neovascularization segmentation in intravascular optical coherence tomography
Structure attention co-training neural network for neovascularization segmentation in intravascular optical coherence tomography
复制标题
用于血管内光学相干断层扫描中新生血管分割的结构注意协同训练神经网络。
DOI：
10.1002/mp.15477
复制
发表时间：
2022
期刊：
影响因子：
3.8
通讯作者：
Jie Tian
中科院分区：
文献类型：
--
作者：
Xiangjun Wu;Yingqian Zhang;Peng Zhang;Hui Hui;Jing Jing;Feng Tian;Jingying Jiang;Xin Yang;Yundai Chen;Jie Tian
PurposeTo develop and validate a neovascularization（NV）segmentation model in intravascular optical coherence tomography（IVOCT）through deep learning methods.Methods and materialsA total 1950 2D slices of 70 IVOCT pullbacks was used in our study.我们从44名患者中随机选择1273个二维切片作为训练集，从11名患者中随机选择379个二维切片作为验证集，从最后15名患者中随机选择298个二维切片作为测试集。自动NV分割具有相当大的挑战性，因为它必须解决斑点噪声，阴影伪影，高分布变化等问题。为了应对这些挑战，基于具有集成结构注意力机制的协同训练架构开发了一种新的基于深度学习的分割方法。开发了协同训练以利用三个连续切片的特征。结构注意力机制包括空间和通道注意力模块，并在每个上采样步骤中集成到协同训练架构中。级联的固定网络进一步纳入，以实现在图像级别的分割在粗到细的方式。ResultsExtensive experiments were performed involving a comparison with several state-of-the-art deep learning-based segmentation methods.此外，还考察了分割结果与人工分割结果的一致性。我们提出的NV自动分割方法与介入心脏病专家手动描绘的相关性最高（Pearson相关系数为0.825）。ConclusionIn this work，我们提出了一种具有集成结构注意机制的共训练架构来分割IVOCT图像中的NV。我们的分割结果和手动分割之间的良好一致性表明，所提出的方法在NV相关斑块诊断和治疗的临床研究中具有很大的应用潜力。
PurposeTo development and validate a neovascularization (NV) segmentation model in intravascular optical coherence tomography (IVOCT) through deep learning methods.Methods and materialsA total of 1950 2D slices of 70 IVOCT pullbacks were used in our study. We randomly selected 1273 2D slices from 44 patients as the training set, 379 2D slices from 11 patients as the validation set, and 298 2D slices from the last 15 patients as the testing set. Automatic NV segmentation is quite challenging, as it must address issues of speckle noise, shadow artifacts, high distribution variation, etc. To meet these challenges, a new deep learning‐based segmentation method is developed based on a co‐training architecture with an integrated structural attention mechanism. Co‐training is developed to exploit the features of three consecutive slices. The structural attention mechanism comprises spatial and channel attention modules and is integrated into the co‐training architecture at each up‐sampling step. A cascaded fixed network is further incorporated to achieve segmentation at the image level in a coarse‐to‐fine manner.ResultsExtensive experiments were performed involving a comparison with several state‐of‐the‐art deep learning‐based segmentation methods. Moreover, the consistency of the results with those of manual segmentation was also investigated. Our proposed NV automatic segmentation method achieved the highest correlation with the manual delineation by interventional cardiologists (the Pearson correlation coefficient is 0.825).ConclusionIn this work, we proposed a co‐training architecture with an integrated structural attention mechanism to segment NV in IVOCT images. The good agreement between our segmentation results and manual segmentation indicates that the proposed method has great potential for application in the clinical investigation of NV‐related plaque diagnosis and treatment.