# Recurrent neural networks for aortic image sequence segmentation with sparse annotations

arXiv: https://arxiv.org/abs/1808.00273 · HTML: https://mlsanity.com/paper/1808.00273/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2018.

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

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- [Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data](https://mlsanity.com/paper/2303.14080/) (2023)
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- [Convolution-Free Medical Image Segmentation using Transformers](https://mlsanity.com/paper/2102.13645/) (2021)
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- [ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation](https://mlsanity.com/paper/2206.02118/) (2022)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation](https://mlsanity.com/paper/2101.09321/) (2021)
- [Weakly Supervised Few-Shot Segmentation Via Meta-Learning](https://mlsanity.com/paper/2109.01693/) (2021)
- [Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations](https://mlsanity.com/paper/2312.01726/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images](https://mlsanity.com/paper/2309.08481/) (2023)
- [ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentation](https://mlsanity.com/paper/2406.13237/) (2024)
- [AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows](https://mlsanity.com/paper/2503.12515/) (2025)
- [Partial annotations for the segmentation of large structures with low annotation cost](https://mlsanity.com/paper/2209.12216/) (2022)
- [Sparse annotation strategies for segmentation of short axis cardiac MRI](https://mlsanity.com/paper/2307.12619/) (2023)
- [MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models](https://mlsanity.com/paper/2404.16174/) (2024)
- [Lost in Tracking: Uncertainty-guided Cardiac Cine MRI Segmentation at Right Ventricle Base](https://mlsanity.com/paper/2410.03320/) (2024)
- [Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised Learning](https://mlsanity.com/paper/2411.05900/) (2024)
- [MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.22890/) (2025)
- [An atrium segmentation network with location guidance and siamese adjustment](https://mlsanity.com/paper/2301.04401/) (2023)
- [INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses](https://mlsanity.com/paper/2309.02147/) (2023)
- [Natural Language Querying System Through Entity Enrichment](https://mlsanity.com/paper/2410.15753/) (2024)
- [Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking](https://mlsanity.com/paper/2608.23879/) (2026)

## Abstract (arXiv)

Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct application of a segmentation algorithm to each time frame of a sequence may ignore the temporal continuity inherent in the sequence. In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task. A key challenge in training this network is that the available manual annotations are temporally sparse, which forbids end-to-end training. We address this challenge by performing non-rigid label propagation on the annotations and introducing an exponentially weighted loss function for training. Experiments on aortic MR image sequences demonstrate that the proposed method significantly improves both accuracy and temporal smoothness of segmentation, compared to a baseline method that utilises spatial information only. It achieves an average Dice metric of 0.960 for the ascending aorta and 0.953 for the descending aorta.