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.
翻译:近年来,3D进化神经网络已成为体积医学图像分割的主要方法,然而,与2D网络相比,3D网络引入了更多的培训参数和对GPU内存的更高要求,这已成为设计和培训高分辨率体积图像3D网络的一个主要限制因素。在这项工作中,我们提议为3D高分辨率图像分割建立一个新的记忆高效网络结构。该网络通过两个阶段的U-net级联框架和第一阶段开发了一种记忆高效的U-net(MeU-net),两个阶段所学的特征通过后编集连接起来,从而进一步改善信息流动。拟议的分解方法是在超高分辨率微分解数据集上评价的,每卷有2.5亿个氧化氮。实验显示,在分解精度和记忆效率方面,它都超过了最先进的3D分解方法。