融合时序监督和注意力机制的脉络膜新生血管分割

Choroidal Neovascularization Segmentation Combining Temporal Supervision and Attention Mechanism

脉络膜新生血管(Choroidal Neovascularization,CNV)一般出现在老年性黄斑变性(Age-related macular degeneration,AMD)晚期,在光学相干断层成像(SD-OCT)中对CNV进行准确分割对AMD的诊疗具有重要意义。文中提出了一种融合时序模型与注意力机制的CNV分割网络。该方法将连续的SD-OCT图像输入分割网络,在编码器部分提取图片多尺度信息,为了更好地提取图片局部特征,又在跳跃连接部分加入注意力门;同时,为了解决分割不连续的问题,在分割网络池化后加入了时序约束网络以构建相邻帧连续性约束,并在损失函数中加入梯度约束以更好地保留病变边界;采用空间金字塔将两部分网络特征图融合以产生分割损失,提高了最终的分割精度。基于患者独立性对12名患者的200组眼睛数据进行实验,该方法的Dice系数为76.3%,overlap达到60.7%,能够在SD-OCT图像中对CNV进行可靠的分割。

Choroidal neovascularization(CNV)generally occurs at the late stage of senile macular degeneration(AMD),and accurate segmentation of CNV in optical coherence tomography(SD-OCT)is of great significance for the diagnosis and treatment of AMD.This paper proposes a CNV multi-task segmentation network that combines time series model and attention mechanism.The continuous SD-OCT image is input into the segmentation network,and the multi-scale information of the picture is extracted in the encoder part.In order to better extract the local features of the picture,the attention gate is added in the skip connection part.In order to solve the problem of discontinuous scanning segmentation,after the segmentation network is pooled,the timing constraint network is passed to generate the continuity constraint of adjacent frames and gradient constraints are added to the loss function to better preserve the lesion boundary.The spatial pyramid is used to fuse the two parts of the network feature map to produce segmentation loss,which improves the final segmentation accuracy.Based on patient independence,effective cross-validation is performed on 200 eyes of 12 patients.The Dice coefficient reaches 76.3%and the overlap reaches 60.7%.CNV can be reliably segmented in SD-OCT images.