# Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

arXiv: https://arxiv.org/abs/1608.03974 · HTML: https://mlsanity.com/paper/1608.03974/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2016.

## Built on by (most cited first, 16 of 16)

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- [Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks](https://mlsanity.com/paper/2109.04188/) (2021)
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- [Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography](https://mlsanity.com/paper/2207.06330/) (2022)
- [Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI](https://mlsanity.com/paper/2601.00794/) (2026)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [AI in Telemedicine: An Appraisal on Deep Learning-Based Approaches to Virtual Diagnostic Solutions (VDS)](https://mlsanity.com/paper/2208.04690/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Scoliosis Detection using Deep Neural Network](https://mlsanity.com/paper/2210.17269/) (2022)
- [Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI using Histogram Matching](https://mlsanity.com/paper/2302.11200/) (2023)
- [Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation](https://mlsanity.com/paper/2404.01723/) (2024)
- [Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation](https://mlsanity.com/paper/2503.17896/) (2025)
- [CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI](https://mlsanity.com/paper/2505.16452/) (2025)

## Abstract (arXiv)

In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.