# Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/1609.01006 · HTML: https://mlsanity.com/paper/1609.01006/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2016.

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

- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [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)
- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain](https://mlsanity.com/paper/2208.14360/) (2022)
- [Learning With Context Feedback Loop for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.02844/) (2021)
- [Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2](https://mlsanity.com/paper/2505.01854/) (2025)
- [Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network](https://mlsanity.com/paper/2206.06341/) (2022)
- [Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation](https://mlsanity.com/paper/2201.00317/) (2022)
- [Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2209.12440/) (2022)
- [Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges](https://mlsanity.com/paper/2211.14732/) (2022)
- [A Topology-Attention ConvLSTM Network and Its Application to EM Images](https://mlsanity.com/paper/2202.03430/) (2022)
- [EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring](https://mlsanity.com/paper/2204.03014/) (2022)
- [Segmenting mechanically heterogeneous domains via unsupervised learning](https://mlsanity.com/paper/2308.15697/) (2023)
- [MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation](https://mlsanity.com/paper/2401.11856/) (2024)
- [MPBD-LSTM: A Predictive Model for Colorectal Liver Metastases Using Time Series Multi-phase Contrast-Enhanced CT Scans](https://mlsanity.com/paper/2412.01973/) (2024)
- [2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks](https://mlsanity.com/paper/2103.05259/) (2021)
- [YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy](https://mlsanity.com/paper/2207.06215/) (2022)
- [Differential-UMamba: Rethinking Tumor Segmentation Under Limited Data Scenarios](https://mlsanity.com/paper/2507.18177/) (2025)
- [Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound](https://mlsanity.com/paper/2107.13833/) (2021)
- [From augmented microscopy to the topological transformer: a new approach in cell image analysis for Alzheimer's research](https://mlsanity.com/paper/2108.01625/) (2021)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learning](https://mlsanity.com/paper/2204.09710/) (2022)
- [Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation](https://mlsanity.com/paper/2404.01723/) (2024)
- [3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence](https://mlsanity.com/paper/2502.01890/) (2025)
- [T-Gated Adapter: A Lightweight Temporal Adapter for Vision-Language Medical Segmentation](https://mlsanity.com/paper/2604.08167/) (2026)

## Abstract (arXiv)

Segmentation of 3D images is a fundamental problem in biomedical image analysis. Deep learning (DL) approaches have achieved state-of-the-art segmentation perfor- mance. To exploit the 3D contexts using neural networks, known DL segmentation methods, including 3D convolution, 2D convolution on planes orthogonal to 2D image slices, and LSTM in multiple directions, all suffer incompatibility with the highly anisotropic dimensions in common 3D biomedical images. In this paper, we propose a new DL framework for 3D image segmentation, based on a com- bination of a fully convolutional network (FCN) and a recurrent neural network (RNN), which are responsible for exploiting the intra-slice and inter-slice contexts, respectively. To our best knowledge, this is the first DL framework for 3D image segmentation that explicitly leverages 3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal Structure Segmentation Challenge and in-house image stacks for 3D fungus segmentation, our approach achieves promising results comparing to the known DL-based 3D segmentation approaches.