  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.
