U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. By combining a convolutional feature extractor with an implicit localization network, our implicit U-Net has 40% less parameters than the equivalent U-Net. Moreover, we propose training and inference procedures to capitalize sparse predictions. When comparing to an equivalent fully convolutional U-Net, Implicit U-Net reduces by approximately 30% inference and training time as well as training memory footprint while achieving comparable results in our experiments with two different abdominal CT scan datasets.
翻译:U-Net一直是医学图像分割任务的上至结构,然而,在将 U-Net 架构扩展至 3D 图像时会出现计算上的挑战。 我们提议了隐含 U-Net 架构, 使高效的隐含代表模式适应受监督的图像分割任务。 我们的隐含 U-Net 结合了一个革命性特征提取器和一个隐含的本地化网络, 其参数比对应的 U- Net 少40%。 此外, 我们提议培训和推断程序, 以利用稀少的预测。 与完全革命性的 U- Net 相比, 隐含 U- Net 将大约30% 的推断和培训时间以及培训记忆足迹, 同时在用两种不同的腹部CT 扫描数据集进行实验时取得可比的结果 。