# 3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation

arXiv: https://arxiv.org/abs/1804.09400 · HTML: https://mlsanity.com/paper/1804.09400/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge](https://mlsanity.com/paper/2301.03281/) (2023)
- [Direct Cardiac Segmentation from Undersampled K-space Using Transformers](https://mlsanity.com/paper/2406.00192/) (2024)
- [Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions](https://mlsanity.com/paper/2105.13153/) (2021)
- [Compositional Segmentation of Cardiac Images Leveraging Metadata](https://mlsanity.com/paper/2410.23130/) (2024)
- [About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations](https://mlsanity.com/paper/2105.14117/) (2021)
- [Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach](https://mlsanity.com/paper/2206.01746/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry](https://mlsanity.com/paper/2303.11676/) (2023)
- [μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation](https://mlsanity.com/paper/2406.16724/) (2024)
- [Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies](https://mlsanity.com/paper/2412.12853/) (2024)
- [Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation](https://mlsanity.com/paper/2503.17896/) (2025)

## Abstract (arXiv)

We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. 3D-consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from UK Biobank. It is then tested on 756 different cases from UK Biobank and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with 30 cases, RVSC with 16 cases). Results comparable or even better than the state-of-the-art in terms of distance measures are achieved. They also emphasize the assets of our method, namely enhanced spatial consistency (currently neither considered nor achieved by the state-of-the-art), and the generalization ability to unseen cases even from other databases.