Image segmentation is a fundamental task in image analysis and clinical practice. The current state-of-the-art techniques are based on U-shape type encoder-decoder networks with skip connections, called U-Net. Despite the powerful performance reported by existing U-Net type networks, they suffer from several major limitations. Issues include the hard coding of the receptive field size, compromising the performance and computational cost, as well as the fact that they do not account for inherent noise in the data. They have problems associated with discrete layers, and do not offer any theoretical underpinning. In this work we introduce continuous U-Net, a novel family of networks for image segmentation. Firstly, continuous U-Net is a continuous deep neural network that introduces new dynamic blocks modelled by second order ordinary differential equations. Secondly, we provide theoretical guarantees for our network demonstrating faster convergence, higher robustness and less sensitivity to noise. Thirdly, we derive qualitative measures to tailor-made segmentation tasks. We demonstrate, through extensive numerical and visual results, that our model outperforms existing U-Net blocks for several medical image segmentation benchmarking datasets.
翻译:图像分割是图像分析和临床实践的一项基本任务。 目前的最先进的技术基于U- shape 类型编码器- 编码器网络,称为U- Net。 尽管现有的 U-Net 类型网络报告了强大的性能,但它们受到若干重大限制。 问题包括: 接收字段大小的硬编码, 影响性能和计算成本, 以及它们没有考虑到数据中固有的噪音。 它们与离散层有问题, 没有提供任何理论依据。 在这项工作中, 我们引入了连续的 U- Net, 一个图像分割网络的新组合。 首先, 持续的 U- Net是一个连续的深神经网络, 以第二顺序普通差异方程式为模型, 引入新的动态区块。 第二, 我们为我们的网络提供理论保证, 显示更快的趋同性、 更高强度和较少对噪音的敏感度。 第三, 我们通过广泛的数字和视觉结果, 我们通过模型超越了现有的一些医学图像分割基准数据集的 U-Net 。