DoubleU-Net ++：利用多尺度特征进行椎骨分割的体系结构
DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation
摘要：
脊椎的准确分割是各种医疗应用（例如，远程手术）中辅助外科医生的重要先决条件。随着深度神经网络的成功开发，最近的研究集中在椎骨分割的基本规则上。先前的作品包含大量的参数，并且它们的分割仅限于一个视图。受DoubleU-Net的启发，提出了一种新的模型DoubleU-Net++，该模型以DensNet作为特征提取器，采用卷积块注意力模块（CBAM）中的特殊注意力模块和金字塔挤压注意力模块（PSA）来改进提取的特征。我们在VerSe 2020和xVertSeg数据集的三个不同视图（矢状、冠状和轴向）上评估了我们提出的模型。与最先进的研究相比，我们的体系结构训练速度更快，并实现了更高的精度，召回率和F1评分作为评估（提高了4-6%），并且对于VerSe 2020数据集，矢状面视图的结果超过94%，冠状面视图的结果超过94%，轴向视图的结果超过93%。此外，对于xVertSeg数据集，我们在矢状面视图中实现了97%以上的精确度、召回率和F1评分，在冠状面视图中实现了93%以上的精确度、召回率和F1评分，在轴向视图中实现了96%以上的精确度、召回率和F1评分。
英文摘要：
Accurate segmentation of the vertebra is an important prerequisite in various medical applications (E.g. tele surgery) to assist surgeons. Following the successful development of deep neural networks, recent studies have focused on the essential rule of vertebral segmentation. Prior works contain a large number of parameters, and their segmentation is restricted to only one view. Inspired by DoubleU-Net, we propose a novel model named DoubleU-Net++ in which DensNet as feature extractor, special attention module from Convolutional Block Attention on Module (CBAM) and, Pyramid Squeeze Attention (PSA) module are employed to improve extracted features. We evaluate our proposed model on three different views (sagittal, coronal, and axial) of VerSe2020 and xVertSeg datasets. Compared with state-of-the-art studies, our architecture is trained faster and achieves higher precision, recall, and F1-score as evaluation (imporoved by 4-6%) and the result of above 94% for sagittal view and above 94% for both coronal view and above 93% axial view were gained for VerSe2020 dataset, respectively. Also, for xVertSeg dataset, we achieved precision, recall,and F1-score of above 97% for sagittal view, above 93% for coronal view ,and above 96% for axial view.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
