# M3D-NCA: Robust 3D Segmentation with Built-in Quality Control

arXiv: https://arxiv.org/abs/2309.02954 · HTML: https://mlsanity.com/paper/2309.02954/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [Cellular automata as convolutional neural networks](https://mlsanity.com/paper/1809.02942/) (2018)
- [Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata](https://mlsanity.com/paper/2302.03473/) (2023)
- [Image segmentation via Cellular Automata](https://mlsanity.com/paper/2008.04965/) (2020)

## Built on by (most cited first, 13 of 13)

- [An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis](https://mlsanity.com/paper/2407.03018/) (2024)
- [Unsupervised Training of Neural Cellular Automata on Edge Devices](https://mlsanity.com/paper/2407.18114/) (2024)
- [NCA-Morph: Medical Image Registration with Neural Cellular Automata](https://mlsanity.com/paper/2410.22265/) (2024)
- [OctreeNCA: Single-Pass 184 MP Segmentation on Consumer Hardware](https://mlsanity.com/paper/2508.06993/) (2025)
- [NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation](https://mlsanity.com/paper/2410.23368/) (2024)
- [MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation](https://mlsanity.com/paper/2501.02447/) (2025)
- [rNCA: Self-Repairing Segmentation Masks](https://mlsanity.com/paper/2512.13397/) (2025)
- [Measuring Prediction Uncertainty in Neural Cellular Automata](https://mlsanity.com/paper/2605.26726/) (2026)
- [Sterilizable Scene Graph Generation for Operating Rooms](https://mlsanity.com/paper/2608.16469/) (2026)
- [QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction](https://mlsanity.com/paper/2412.07156/) (2024)
- [eNCApsulate: NCA for Precision Diagnosis on Capsule Endoscopes](https://mlsanity.com/paper/2504.21562/) (2025)
- [SMART: A Flexible, Interpretable, and Scalable Spatio-temporal Brain Atlas from High-Resolution Imaging Data](https://mlsanity.com/paper/2606.18753/) (2026)
- [Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities](https://mlsanity.com/paper/2609.24595/) (2026)

## Abstract (arXiv)

Medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures. However, the real-world utility of such models is limited by their high computational requirements, which makes them impractical for resource-constrained environments such as primary care facilities and conflict zones. Furthermore, shifts in the imaging domain can render these models ineffective and even compromise patient safety if such errors go undetected. To address these challenges, we propose M3D-NCA, a novel methodology that leverages Neural Cellular Automata (NCA) segmentation for 3D medical images using n-level patchification. Moreover, we exploit the variance in M3D-NCA to develop a novel quality metric which can automatically detect errors in the segmentation process of NCAs. M3D-NCA outperforms the two magnitudes larger UNet models in hippocampus and prostate segmentation by 2% Dice and can be run on a Raspberry Pi 4 Model B (2GB RAM). This highlights the potential of M3D-NCA as an effective and efficient alternative for medical image segmentation in resource-constrained environments.