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

arXiv: https://arxiv.org/abs/2008.07071 · HTML: https://mlsanity.com/paper/2008.07071/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Multi-Exit Semantic Segmentation Networks](https://mlsanity.com/paper/2106.03527/) (2021)
- [Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search](https://mlsanity.com/paper/2107.06871/) (2021)
- [RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2206.04682/) (2022)
- [A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus](https://mlsanity.com/paper/2206.07089/) (2022)

## Abstract (arXiv)

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.