# Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation

arXiv: https://arxiv.org/abs/1908.09298 · HTML: https://mlsanity.com/paper/1908.09298/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation](https://mlsanity.com/paper/2103.08219/) (2021)
- [Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives](https://mlsanity.com/paper/2106.15707/) (2021)
- [Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation](https://mlsanity.com/paper/2101.01513/) (2021)
- [From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2504.11368/) (2025)
- [Multi-Modality Cardiac Image Analysis with Deep Learning](https://mlsanity.com/paper/2111.04736/) (2021)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)

## Abstract (arXiv)

Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.