Abstract：
We propose a shape prior representation-constrained multi-scale features fusion segmentation network for medical image segmentation, including training and testing stages. The novelty of our training framework lies in two modules comprised of the shape prior constraint and the multi-scale features fusion. The shape prior learning model is embedded into a segmentation neural network to solve the problems of low contrast and neighboring organs with intensities similar to the target organ. The latter can provide both local and global contexts to address the issues of large variations in patient postures as well as organ\'s shape. In the testing stage, we propose a circular collaboration framework strategy which combines a shape generator auto-encoder network model with a segmentation network model, allowing the two models to collaborate with each other, resulting in a cooperative effect that leads to accurate segmentations. Our proposed method is evaluated and demonstrated on the ACDC MICCAI\'17 Challenge Dataset, CT scans datasets, namely, in COVID-19 CT lung, and LiTS2017 liver from three different datasets, and its results are compared with the recent state of the art in these areas. Our method ranked 1st on the ACDC Dataset in terms of Dice score and achieved very competitive performance on COVID-19 CT lung and LiTS2017 liver segmentation.
摘要：
我们提出了一种形状先验表示约束的多尺度特征融合分割网络，用于医学图像分割，包括培训和测试阶段。我们的训练框架的新颖性在于由形状先验约束和多尺度特征融合组成的两个模块。将形状先验学习模型嵌入到分割神经网络中，以解决对比度低，邻近器官强度与目标器官相似的问题。后者可以提供本地和全球环境，以解决患者姿势以及器官形状的大变化问题。在测试阶段,我们提出了一种循环协作框架策略，该策略将形状生成器自动编码器网络模型与分段网络模型相结合，允许两个模型相互合作，导致精确分割的合作效应。我们提出的方法在ACDCMICCAI'17挑战数据集上进行了评估和演示，CT扫描数据集,即,在COVID-19CT肺部，和来自三个不同数据集的LITS2017肝脏，并将其结果与这些领域的最新技术进行比较。我们的方法在Dice评分方面在ACDC数据集上排名第一，在COVID-19CT肺部和LiTS2017肝脏分割方面取得了非常有竞争力的表现。
