# Implicit U-Net for volumetric medical image segmentation

arXiv: https://arxiv.org/abs/2206.15217 · HTML: https://mlsanity.com/paper/2206.15217/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation](https://mlsanity.com/paper/2106.15097/) (2021)
- [RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans](https://mlsanity.com/paper/1811.01328/) (2018)
- [Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities](https://mlsanity.com/paper/1704.03379/) (2017)
- [Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation](https://mlsanity.com/paper/1702.00045/) (2017)
- [Implicit field learning for unsupervised anomaly detection in medical images](https://mlsanity.com/paper/2106.05214/) (2021)
- [Efficient Context-Aware Network for Abdominal Multi-organ Segmentation](https://mlsanity.com/paper/2109.10601/) (2021)

## Built on by (most cited first, 4 of 4)

- [ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction](https://mlsanity.com/paper/2312.06164/) (2023)
- [Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions](https://mlsanity.com/paper/2505.08919/) (2025)
- [End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data](https://mlsanity.com/paper/2509.12068/) (2025)
- [Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications](https://mlsanity.com/paper/2609.01427/) (2026)

## Abstract (arXiv)

U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. By combining a convolutional feature extractor with an implicit localization network, our implicit U-Net has 40% less parameters than the equivalent U-Net. Moreover, we propose training and inference procedures to capitalize sparse predictions. When comparing to an equivalent fully convolutional U-Net, Implicit U-Net reduces by approximately 30% inference and training time as well as training memory footprint while achieving comparable results in our experiments with two different abdominal CT scan datasets.