论文标题
迈向心脏干预帮助:实时3D心脏Cine MRI细分的硬件感知神经体系结构探索
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation
论文作者
论文摘要
实时心脏磁共振成像(MRI)在指导各种心脏干预方面起着越来越重要的作用。为了提供更好的视觉援助,需要对CINE MRI框架进行直接分割,以避免明显的视觉滞后。此外,考虑到可靠性和患者数据隐私,最好在本地硬件上进行计算。最先进的MRI细分方法主要仅关注准确性,并且几乎不能用于实时应用程序或本地硬件。在这项工作中,我们介绍了实时3D心脏Cine MRI细分的第一个硬件感知的多尺度神经体系结构搜索(NAS)框架。提出的框架将延迟正则术语纳入损耗函数中以处理实时约束,并考虑基础硬件。此外,相对于体系结构参数,该公式是完全差异的,因此随机梯度下降(SGD)可用于优化,以降低计算成本,同时保持优化质量。 ACDC MICCAI 2017数据集的实验结果表明,与最新的NAS分割框架相比,我们的硬件感知的多尺度NAS框架可以将延迟降低高达3.5倍,并满足实时限制,同时仍能达到竞争性分割精度。
Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the cine MRI frames need to be segmented on-the-fly to avoid noticeable visual lag. In addition, considering reliability and patient data privacy, the computation is preferably done on local hardware. State-of-the-art MRI segmentation methods mostly focus on accuracy only, and can hardly be adopted for real-time application or on local hardware. In this work, we present the first hardware-aware multi-scale neural architecture search (NAS) framework for real-time 3D cardiac cine MRI segmentation. The proposed framework incorporates a latency regularization term into the loss function to handle real-time constraints, with the consideration of underlying hardware. In addition, the formulation is fully differentiable with respect to the architecture parameters, so that stochastic gradient descent (SGD) can be used for optimization to reduce the computation cost while maintaining optimization quality. Experimental results on ACDC MICCAI 2017 dataset demonstrate that our hardware-aware multi-scale NAS framework can reduce the latency by up to 3.5 times and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.