IEEE Trans Med Imaging. 2019 May;38(5):1207-1215. doi: 10.1109/TMI.2018.2881678. Epub 2018 Nov 16.
Segmentation in 3-D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D approaches based on convolutional neural networks usually suffer from at least three main issues caused predominantly by implementation constraints-first, they require resizing the volume to the lower-resolutional reference dimensions, and second, the capacity of such approaches is very limited due to memory restrictions, and third, all slices of volumes have to be available at any given training or testing time. We address these problems by a U-Net-like architecture consisting of bidirectional convolutional long short-term memory and convolutional, pooling, upsampling, and concatenation layers enclosed into time-distributed wrappers. Our network can either process the full volumes in a sequential manner or segment slabs of slices on demand. We demonstrate performance of our architecture on vertebrae and liver segmentation tasks in 3-D computed tomography scans.
在当前的临床实践中，三维扫描中的分割在支持诊断、组织量化或治疗计划方面发挥着越来越重要的作用。目前基于卷积神经网络的三维方法主要存在至少三个问题，这些问题主要是由实现限制引起的：首先，它们需要将体积调整为较低分辨率的参考维度；其次，由于内存限制，此类方法的容量非常有限；第三，在任何给定的训练或测试时间，都必须提供体积的所有切片。我们通过使用由双向卷积长短时记忆体和卷积、池化、上采样以及连接层组成的类似于 U-Net 的架构来解决这些问题，并将这些层封装到时间分布式包装器中。我们的网络可以按顺序处理整个体积，也可以根据需要对切片块进行分割。我们在三维计算机断层扫描中的椎骨和肝脏分割任务上展示了我们的架构的性能。
