论文标题
ICA-UNET：ICA启发了实时3D心脏Cine MRI分段的统计UNET
ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation
论文作者
论文摘要
实时Cine磁共振成像（MRI）在各种心脏干预措施中起着越来越重要的作用。为了快速，准确的视觉援助，需要对时间框架进行分割。但是，最先进的MRI分割方法是由于其高计算复杂性或实时的，但准确性损失和潜伏期的增加（导致视觉上明显的滞后）使用了离线。因此，它们几乎无法采用来协助视觉指导。在这项工作中，我们受到对学习独立组件分析（ICA）的新解释的启发，我们提出了一种新型的ICA-UNET，用于实时3D心脏Cine MRI分割。使用MICCAI ACDC 2017数据集进行的实验表明，与最先进的ICA-UNET相比，ICA-UNET不仅可以达到更高的骰子分数，而且还满足了吞吐量和延迟的实时要求（降低12.6倍），从而实现了无视觉下降的实时指导。
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the temporal frames need to be segmented on-the-fly. However, state-of-the-art MRI segmentation methods are used either offline because of their high computation complexity, or in real-time but with significant accuracy loss and latency increase (causing visually noticeable lag). As such, they can hardly be adopted to assist visual guidance. In this work, inspired by a new interpretation of Independent Component Analysis (ICA) for learning, we propose a novel ICA-UNet for real-time 3D cardiac cine MRI segmentation. Experiments using the MICCAI ACDC 2017 dataset show that, compared with the state-of-the-arts, ICA-UNet not only achieves higher Dice scores, but also meets the real-time requirements for both throughput and latency (up to 12.6X reduction), enabling real-time guidance for cardiac interventions without visual lag.
