# NISF: Neural Implicit Segmentation Functions

arXiv: https://arxiv.org/abs/2309.08643 · HTML: https://mlsanity.com/paper/2309.08643/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2023.

## Built on by (most cited first, 29 of 29)

- [I-MedSAM: Implicit Medical Image Segmentation with Segment Anything](https://mlsanity.com/paper/2311.17081/) (2023)
- [Deep Generative Models for 3D Medical Image Synthesis](https://mlsanity.com/paper/2410.17664/) (2024)
- [Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond](https://mlsanity.com/paper/2504.13037/) (2025)
- [Fit Pixels, Get Labels: Meta-learned Implicit Networks for Image Segmentation](https://mlsanity.com/paper/2510.04021/) (2025)
- [Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images](https://mlsanity.com/paper/2406.00329/) (2024)
- [Spatio-temporal neural distance fields for conditional generative modeling of the heart](https://mlsanity.com/paper/2407.10663/) (2024)
- [MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fields](https://mlsanity.com/paper/2502.14401/) (2025)
- [HyperSpace: Hypernetworks for spacing-adaptive image segmentation](https://mlsanity.com/paper/2407.03681/) (2024)
- [Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations](https://mlsanity.com/paper/2512.19316/) (2025)
- [Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction](https://mlsanity.com/paper/2508.13826/) (2025)
- [Implicit Neural Representations for Speed-of-Sound Estimation in Ultrasound](https://mlsanity.com/paper/2409.14035/) (2024)
- [HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation](https://mlsanity.com/paper/2507.08205/) (2025)
- [OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations](https://mlsanity.com/paper/2603.08279/) (2026)
- [Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction](https://mlsanity.com/paper/2605.04234/) (2026)
- [ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction](https://mlsanity.com/paper/2312.06164/) (2023)
- [MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction](https://mlsanity.com/paper/2412.10985/) (2024)
- [Generating visual explanations from deep networks using implicit neural representations](https://mlsanity.com/paper/2501.11784/) (2025)
- [Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations](https://mlsanity.com/paper/2508.05168/) (2025)
- [WSI-INR: Implicit Neural Representations for Lesion Segmentation in Whole-Slide Images](https://mlsanity.com/paper/2603.03749/) (2026)
- [NISF++: Geometrically-grounded implicit representations of 3D+time cardiac function from 2D short- and long-axis MR views](https://mlsanity.com/paper/2608.00752/) (2026)
- [NeuralOCT: Airway OCT Analysis via Neural Fields](https://mlsanity.com/paper/2403.10622/) (2024)
- [Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation](https://mlsanity.com/paper/2408.11754/) (2024)
- [Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning](https://mlsanity.com/paper/2509.01412/) (2025)
- [Don't Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysis](https://mlsanity.com/paper/2601.02447/) (2026)
- [What Cohort INRs Encode and Where to Freeze Them](https://mlsanity.com/paper/2605.08298/) (2026)
- [Implicit representations are dead. Long live explicit primitives!](https://mlsanity.com/paper/2608.10001/) (2026)
- [Modelling Geographic Atrophy Progression using Implicit Neural Representations](https://mlsanity.com/paper/2608.10807/) (2026)
- [Observation-Conditioned Latent Energy Priors for Sparse Implicit Neural Shape Completion](https://mlsanity.com/paper/2609.03694/) (2026)
- [MRI-Guided Reslice-Refined Cross-Slice SDF Reconstruction of the Left Ventricle from Cardiac MRI with Sparse Axial Supervision](https://mlsanity.com/paper/2609.08148/) (2026)

## Abstract (arXiv)

Segmentation of anatomical shapes from medical images has taken an important role in the automation of clinical measurements. While typical deep-learning segmentation approaches are performed on discrete voxels, the underlying objects being analysed exist in a real-valued continuous space. Approaches that rely on convolutional neural networks (CNNs) are limited to grid-like inputs and not easily applicable to sparse or partial measurements. We propose a novel family of image segmentation models that tackle many of CNNs' shortcomings: Neural Implicit Segmentation Functions (NISF). Our framework takes inspiration from the field of neural implicit functions where a network learns a mapping from a real-valued coordinate-space to a shape representation. NISFs have the ability to segment anatomical shapes in high-dimensional continuous spaces. Training is not limited to voxelized grids, and covers applications with sparse and partial data. Interpolation between observations is learnt naturally in the training procedure and requires no post-processing. Furthermore, NISFs allow the leveraging of learnt shape priors to make predictions for regions outside of the original image plane. We go on to show the framework achieves dice scores of 0.87 ± 0.045 on a (3D+t) short-axis cardiac segmentation task using the UK Biobank dataset. We also provide a qualitative analysis on our frameworks ability to perform segmentation and image interpolation on unseen regions of an image volume at arbitrary resolutions.