论文标题
高分辨率体积Microct图像的记忆效率分割
Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images
论文作者
论文摘要
近年来，3D卷积神经网络已成为体积医学图像分割的主要方法。但是，与他们的2D同行相比，3D网络引入了更多的训练参数和更高的GPU内存需求。这已成为设计和训练3D网络的主要限制因素，以实现高分辨率体积图像。在这项工作中，我们为3D高分辨率图像分割提出了一种新颖的记忆有效网络体系结构。该网络通过基于两阶段的U-NET级联框架同时结合了全球和本地功能，在第一阶段，开发了内存效率的U-NET（MEU-NET）。在两个阶段学习的功能是通过召开后连接的，从而进一步改善了信息流。在超高分辨率的Microct数据集上评估所提出的分割方法，每卷通常为2.5亿个体素。实验表明，就分割精度和记忆效率而言，它的表现优于最先进的3D分割方法。
In recent years, 3D convolutional neural networks have become the dominant approach for volumetric medical image segmentation. However, compared to their 2D counterparts, 3D networks introduce substantially more training parameters and higher requirement for the GPU memory. This has become a major limiting factor for designing and training 3D networks for high-resolution volumetric images. In this work, we propose a novel memory-efficient network architecture for 3D high-resolution image segmentation. The network incorporates both global and local features via a two-stage U-net-based cascaded framework and at the first stage, a memory-efficient U-net (meU-net) is developed. The features learnt at the two stages are connected via post-concatenation, which further improves the information flow. The proposed segmentation method is evaluated on an ultra high-resolution microCT dataset with typically 250 million voxels per volume. Experiments show that it outperforms state-of-the-art 3D segmentation methods in terms of both segmentation accuracy and memory efficiency.
