Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation

Abstract

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 realtime 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 multiscale NAS framework can reduce the latency by up to 3.5× and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.

Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

“…Neural Architecture Search (NAS) [24,26,27] is one of the most successful efforts to address this issue. NAS liberates human labor from endlessly exploring optimal handcrafted DNN models by automatically identifying neural architectures that can offer desired performances from a pre-determined search space.…”