# Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning

arXiv: https://arxiv.org/abs/2105.12722 · HTML: https://mlsanity.com/paper/2105.12722/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [MAST: A Memory-Augmented Self-supervised Tracker](https://mlsanity.com/paper/2002.07793/) (2020)
- [FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow](https://mlsanity.com/paper/2101.09639/) (2021)

## Built on by (most cited first, 16 of 16)

- [Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network](https://mlsanity.com/paper/2411.13873/) (2024)
- [Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2](https://mlsanity.com/paper/2505.01854/) (2025)
- [Self-supervised Tumor Segmentation through Layer Decomposition](https://mlsanity.com/paper/2109.03230/) (2021)
- [Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation](https://mlsanity.com/paper/2211.08840/) (2022)
- [Exemplar Learning for Medical Image Segmentation](https://mlsanity.com/paper/2204.01713/) (2022)
- [Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation](https://mlsanity.com/paper/2403.12290/) (2024)
- [Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation](https://mlsanity.com/paper/2312.10978/) (2023)
- [How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?](https://mlsanity.com/paper/2412.08081/) (2024)
- [SIAD: Self-supervised Image Anomaly Detection System](https://mlsanity.com/paper/2208.04173/) (2022)
- [OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2309.13671/) (2023)
- [On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling](https://mlsanity.com/paper/2407.15260/) (2024)
- [BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data](https://mlsanity.com/paper/2505.09829/) (2025)
- [Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model](https://mlsanity.com/paper/2509.15167/) (2025)
- [FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentation](https://mlsanity.com/paper/2601.01687/) (2026)
- [Inference-Time Orthogonal Seeding Enables Geometry-Aligned 3D Organ Segmentation for Slice-Propagation Methods](https://mlsanity.com/paper/2608.12658/) (2026)
- [UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation](https://mlsanity.com/paper/2610.06938/) (2026)

## Abstract (arXiv)

The objective of this work is to segment any arbitrary structures of interest (SOI) in 3D volumes by only annotating a single slice, (i.e. semi-automatic 3D segmentation). We show that high accuracy can be achieved by simply propagating the 2D slice segmentation with an affinity matrix between consecutive slices, which can be learnt in a self-supervised manner, namely slice reconstruction. Specifically, we compare the proposed framework, termed as Sli2Vol, with supervised approaches and two other unsupervised/ self-supervised slice registration approaches, on 8 public datasets (both CT and MRI scans), spanning 9 different SOIs. Without any parameter-tuning, the same model achieves superior performance with Dice scores (0-100 scale) of over 80 for most of the benchmarks, including the ones that are unseen during training. Our results show generalizability of the proposed approach across data from different machines and with different SOIs: a major use case of semi-automatic segmentation methods where fully supervised approaches would normally struggle. The source code will be made publicly available at https://github.com/pakheiyeung/Sli2Vol.