This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and optimized simultaneously for their mutual benefit. An objective function that optimizes spatial correspondence for the segmented structures across time-points is proposed. We applied Segis-Net to the analysis of white matter tracts from N=8045 longitudinal brain MRI datasets of 3249 elderly individuals. Segis-Net approach showed a significant increase in registration accuracy, spatio-temporal segmentation consistency, and reproducibility comparing with two multistage pipelines. This also led to a significant reduction in the sample-size that would be required to achieve the same statistical power in analyzing tract-specific measures. Thus, we expect that Segis-Net can serve as a new reliable tool to support longitudinal imaging studies to investigate macro- and microstructural brain changes over time.
翻译:这项工作为纵向图像分析提供了一个单步深学习框架,即Segis-Net。为最佳利用纵向数据中的现有信息,这一方法同时学习多级分割和非线性登记。分解和注册是使用进化神经网络建模的,同时优化,以相互受益。提出了一个目标功能,优化跨时间点的分块结构的空间通信。我们用Segis-Net分析N=8045 长度脑MRI数据组的白物质片。Segis-Net方法显示登记准确性、时空分解一致性和与两个多阶段管道对比的可复制性显著提高。这也导致样本规模的大幅缩小,这是在分析量度计量方面实现相同统计能力所需要的。因此,我们期望Sgis-Net能够成为支持对3249名老年人进行长度成像研究的新的可靠工具。Segis-Net方法显示,可以调查长期的宏观和微观结构的脑部位变化。