# 4D CNN for semantic segmentation of cardiac volumetric sequences

arXiv: https://arxiv.org/abs/1906.07295 · HTML: https://mlsanity.com/paper/1906.07295/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

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

- [Motion-enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module](https://mlsanity.com/paper/2501.14929/) (2025)
- [CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke](https://mlsanity.com/paper/2303.08757/) (2023)
- [OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation](https://mlsanity.com/paper/2504.09655/) (2025)
- [Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation](https://mlsanity.com/paper/2505.09564/) (2025)
- [Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation](https://mlsanity.com/paper/2403.10009/) (2024)
- [Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model](https://mlsanity.com/paper/2506.02060/) (2025)

## Abstract (arXiv)

We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. While only a small subset of volumes in the temporal sequence is annotated, we define a sparse loss function on available labels to allow the network to leverage unlabeled images during training and generate a fully segmented sequence. We investigate the accuracy of the proposed 4D network to predict temporally consistent segmentations and compare with traditional 3D segmentation approaches. We demonstrate the feasibility of the 4D CNN and establish its performance on cardiac 4D CCTA.