# RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation

arXiv: https://arxiv.org/abs/2206.04682 · HTML: https://mlsanity.com/paper/2206.04682/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 8 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search](https://mlsanity.com/paper/1907.05737/) (2019)
- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [Hardware/Software Co-Exploration of Neural Architectures](https://mlsanity.com/paper/1907.04650/) (2019)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions](https://mlsanity.com/paper/2005.02563/) (2020)
- [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2007.06151/) (2020)
- [HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet](https://mlsanity.com/paper/2112.10652/) (2021)
- [BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2106.14033/) (2021)
- [ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2007.09455/) (2020)
- [MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation](https://mlsanity.com/paper/1909.06726/) (2019)
- [Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2008.07071/) (2020)
- [Accelerated Computing in Magnetic Resonance Imaging -- Real-Time Imaging Using Non-Linear Inverse Reconstruction](https://mlsanity.com/paper/1701.08361/) (2017)
- [Mixed-Block Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2202.11401/) (2022)

## Built on by (most cited first, 3 of 3)

- [Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search](https://mlsanity.com/paper/2304.05405/) (2023)
- [Auto-nnU-Net: Towards Automated Medical Image Segmentation](https://mlsanity.com/paper/2505.16561/) (2025)
- [Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System](https://mlsanity.com/paper/2212.08624/) (2022)

## Abstract (arXiv)

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.