# Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

arXiv: https://arxiv.org/abs/1910.10470 · HTML: https://mlsanity.com/paper/1910.10470/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [LT-OCF: Learnable-Time ODE-based Collaborative Filtering](https://mlsanity.com/paper/2108.06208/) (2021)
- [ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images](https://mlsanity.com/paper/2406.14794/) (2024)
- [ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations](https://mlsanity.com/paper/2105.14953/) (2021)
- [SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation](https://mlsanity.com/paper/2408.00496/) (2024)
- [LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising](https://mlsanity.com/paper/2108.04993/) (2021)
- [Continuous U-Net: Faster, Greater and Noiseless](https://mlsanity.com/paper/2302.00626/) (2023)
- [Semantic Segmentation using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2209.08667/) (2022)
- [Graph Neural Ordinary Differential Equations-based method for Collaborative Filtering](https://mlsanity.com/paper/2311.12329/) (2023)
- [Physics-Informed Machine Learning in Biomedical Science and Engineering](https://mlsanity.com/paper/2510.05433/) (2025)
- [Time and State Dependent Neural Delay Differential Equations](https://mlsanity.com/paper/2306.14545/) (2023)
- [A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation](https://mlsanity.com/paper/2203.00628/) (2022)
- [STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction](https://mlsanity.com/paper/2107.04980/) (2021)
- [Continuous Learned Primal Dual](https://mlsanity.com/paper/2405.02478/) (2024)
- [Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies](https://mlsanity.com/paper/2411.16956/) (2024)

## Abstract (arXiv)

Automated medical image segmentation plays a key role in quantitative research and diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of-the-art. A key disadvantage is the hard-coding of the receptive field size, which requires architecture optimization for each segmentation task. Furthermore, increasing the receptive field results in an increasing number of weights. Recently, Neural Ordinary Differential Equations (NODE) have been proposed, a new type of continuous depth deep neural network. This framework allows for a dynamic receptive field at a fixed memory cost and a smaller amount of parameters. We show on a colon gland segmentation dataset (GlaS) that these NODEs can be used within the U-Net framework to improve segmentation results while reducing memory load and parameter counts.