论文标题
通过无监督的卷积神经网络进行医学图像分割
Medical Image Segmentation via Unsupervised Convolutional Neural Network
论文作者
论文摘要
对于大多数基于学习的分割方法，需要大量高质量的培训数据。在本文中，我们提出了一种基于学习的新型细分模型，可以接受半训练或不受监督。具体而言，在无监督的设置中，我们通过卷积神经网络（Convnet）对活动轮廓（ACWE）框架进行参数化，并使用自我监督的方法优化Convnet的参数。在另一种环境（半监督）中，训练过程中使用了辅助分割地面真相。我们表明，该方法在单光子发射计算机断层扫描（SPECT）图像的背景下提供了快速和高质量的骨分割。
For the majority of the learning-based segmentation methods, a large quantity of high-quality training data is required. In this paper, we present a novel learning-based segmentation model that could be trained semi- or un- supervised. Specifically, in the unsupervised setting, we parameterize the Active contour without edges (ACWE) framework via a convolutional neural network (ConvNet), and optimize the parameters of the ConvNet using a self-supervised method. In another setting (semi-supervised), the auxiliary segmentation ground truth is used during training. We show that the method provides fast and high-quality bone segmentation in the context of single-photon emission computed tomography (SPECT) image.
