# Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography

arXiv: https://arxiv.org/abs/2109.06909 · HTML: https://mlsanity.com/paper/2109.06909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control](https://mlsanity.com/paper/1811.09800/) (2018)
- [Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search](https://mlsanity.com/paper/1901.11211/) (2019)
- [Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth](https://mlsanity.com/paper/1702.03407/) (2017)
- [Real-time Prediction of Segmentation Quality](https://mlsanity.com/paper/1806.06244/) (2018)
- [Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling](https://mlsanity.com/paper/1804.07046/) (2018)
- [Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation](https://mlsanity.com/paper/1806.03106/) (2018)
- [MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation](https://mlsanity.com/paper/1909.06726/) (2019)
- [VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination](https://mlsanity.com/paper/1806.03619/) (2018)
- [Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model](https://mlsanity.com/paper/1806.07497/) (2018)
- [A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation](https://mlsanity.com/paper/1907.12244/) (2019)

## Built on by (most cited first, 1 of 1)

- [Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System](https://mlsanity.com/paper/2212.08624/) (2022)

## Abstract (arXiv)

Automatic myocardial segmentation of contrast echocardiography has shown great potential in the quantification of myocardial perfusion parameters. Segmentation quality control is an important step to ensure the accuracy of segmentation results for quality research as well as its clinical application. Usually, the segmentation quality control happens after the data acquisition. At the data acquisition time, the operator could not know the quality of the segmentation results. On-the-fly segmentation quality control could help the operator to adjust the ultrasound probe or retake data if the quality is unsatisfied, which can greatly reduce the effort of time-consuming manual correction. However, it is infeasible to deploy state-of-the-art DNN-based models because the segmentation module and quality control module must fit in the limited hardware resource on the ultrasound machine while satisfying strict latency constraints. In this paper, we propose a hardware-aware neural architecture search framework for automatic myocardial segmentation and quality control of contrast echocardiography. We explicitly incorporate the hardware latency as a regularization term into the loss function during training. The proposed method searches the best neural network architecture for the segmentation module and quality prediction module with strict latency.