Accurately segmenting temporal frames of cine magnetic resonance imaging (MRI) is a crucial step in various real-time MRI guided cardiac interventions. To achieve fast and accurate visual assistance, there are strict requirements on the maximum latency and minimum throughput of the segmentation framework. State-of-the-art neural networks on this task are mostly hand-crafted to satisfy these constraints while achieving high accuracy. On the other hand, while existing literature have demonstrated the power of neural architecture search (NAS) in automatically identifying the best neural architectures for various medical applications, they are mostly guided by accuracy, sometimes with computation complexity, and the importance of real-time constraints are overlooked. A major challenge is that such constraints are non-differentiable and are thus not compatible with the widely used differentiable NAS frameworks. In this paper, we present a strategy that directly handles real-time constraints in a differentiable NAS framework named RT-DNAS. Experiments on extended 2017 MICCAI ACDC dataset show that compared with state-of-the-art manually and automatically designed architectures, RT-DNAS is able to identify ones with better accuracy while satisfying the real-time constraints.
翻译:另一方面,虽然现有文献表明神经结构搜索(NAS)在自动确定各种医疗应用的最佳神经结构方面的力量,但它们大多以准确性为指导,有时还带有计算复杂性,而且忽视实时限制的重要性。一项重大挑战是,这种限制是不可区别的,因此与广泛使用的可区别的NAS框架不兼容。在本文中,我们提出了一个战略,直接处理称为RT-DNAS的不同NAS框架的实时限制。关于2017年扩展的MICCAI ACDC数据集的实验表明,与最先进的手动和自动设计结构相比,可以更好地确定真实的制约。