# Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

arXiv: https://arxiv.org/abs/1705.08302 · HTML: https://mlsanity.com/paper/1705.08302/

## Key facts

- Cited by 109 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 30 of 109)

- [Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets](https://mlsanity.com/paper/2209.02032/) (2022)
- [Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation](https://mlsanity.com/paper/2306.02416/) (2023)
- [HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees](https://mlsanity.com/paper/2410.10807/) (2024)
- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
- [Deep Implicit Statistical Shape Models for 3D Medical Image Delineation](https://mlsanity.com/paper/2104.02847/) (2021)
- [DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models](https://mlsanity.com/paper/2110.07152/) (2021)
- [Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis](https://mlsanity.com/paper/2203.10977/) (2022)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR](https://mlsanity.com/paper/2107.12689/) (2021)
- [FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images](https://mlsanity.com/paper/2104.01771/) (2021)
- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images](https://mlsanity.com/paper/2307.08535/) (2023)
- [Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound](https://mlsanity.com/paper/2207.02549/) (2022)
- [Anatomically-aware Uncertainty for Semi-supervised Image Segmentation](https://mlsanity.com/paper/2310.16099/) (2023)
- [Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation](https://mlsanity.com/paper/2107.03887/) (2021)
- [Hybrid graph convolutional neural networks for landmark-based anatomical segmentation](https://mlsanity.com/paper/2106.09832/) (2021)
- [CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation](https://mlsanity.com/paper/2206.07664/) (2022)
- [A Comprehensive Review on Deep Supervision: Theories and Applications](https://mlsanity.com/paper/2207.02376/) (2022)
- [CF-Seg: Counterfactuals meet Segmentation](https://mlsanity.com/paper/2506.16213/) (2025)
- [Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity](https://mlsanity.com/paper/2302.02125/) (2023)
- [A Framework for Interpretability in Machine Learning for Medical Imaging](https://mlsanity.com/paper/2310.01685/) (2023)
- [Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation](https://mlsanity.com/paper/2104.05533/) (2021)
- [Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts](https://mlsanity.com/paper/2108.11926/) (2021)
- [MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation](https://mlsanity.com/paper/2207.01883/) (2022)
- [Anatomy-guided Pathology Segmentation](https://mlsanity.com/paper/2407.05844/) (2024)
- [Learning With Context Feedback Loop for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.02844/) (2021)
- [A Generalized Zero-Shot Quantization of Deep Convolutional Neural Networks via Learned Weights Statistics](https://mlsanity.com/paper/2112.02834/) (2021)
- [Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation](https://mlsanity.com/paper/2112.13815/) (2021)
- [Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation](https://mlsanity.com/paper/2201.01636/) (2022)
- [Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI](https://mlsanity.com/paper/2202.09258/) (2022)

## Abstract (arXiv)

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learned non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learned deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.