# Improving the generalizability of convolutional neural network-based segmentation on CMR images

arXiv: https://arxiv.org/abs/1907.01268 · HTML: https://mlsanity.com/paper/1907.01268/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 25 of 25)

- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Anomaly Detection under Distribution Shift](https://mlsanity.com/paper/2303.13845/) (2023)
- [AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation](https://mlsanity.com/paper/2207.13249/) (2022)
- [AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs](https://mlsanity.com/paper/2106.08727/) (2021)
- [Towards a vision foundation model for comprehensive assessment of Cardiac MRI](https://mlsanity.com/paper/2410.01665/) (2024)
- [Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2107.01079/) (2021)
- [Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts](https://mlsanity.com/paper/2211.04393/) (2022)
- [Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation](https://mlsanity.com/paper/2302.02504/) (2023)
- [An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation](https://mlsanity.com/paper/2308.13415/) (2023)
- [Prompt-Based Segmentation at Multiple Resolutions and Lighting Conditions using Segment Anything Model 2](https://mlsanity.com/paper/2408.06970/) (2024)
- [Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images](https://mlsanity.com/paper/2308.15141/) (2023)
- [Domain generalization in deep learning for contrast-enhanced imaging](https://mlsanity.com/paper/2110.07360/) (2021)
- [Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data](https://mlsanity.com/paper/2109.07955/) (2021)
- [Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy](https://mlsanity.com/paper/2210.05673/) (2022)
- [DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation](https://mlsanity.com/paper/2501.03466/) (2025)
- [From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2510.01296/) (2025)
- [Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification](https://mlsanity.com/paper/2111.08701/) (2021)
- [Complex Style Image Transformations for Domain Generalization in Medical Images](https://mlsanity.com/paper/2406.00298/) (2024)
- [Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach](https://mlsanity.com/paper/2509.02918/) (2025)
- [A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis](https://mlsanity.com/paper/2205.01673/) (2022)
- [Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets](https://mlsanity.com/paper/2308.13488/) (2023)
- [Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation](https://mlsanity.com/paper/2311.09737/) (2023)
- [A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation](https://mlsanity.com/paper/2402.14300/) (2024)
- [Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation](https://mlsanity.com/paper/2403.10009/) (2024)
- [Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI](https://mlsanity.com/paper/2411.06911/) (2024)

## Abstract (arXiv)

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task.