  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.
