# Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2007.08826 · HTML: https://mlsanity.com/paper/2007.08826/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 30 of 40)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts](https://mlsanity.com/paper/2109.04379/) (2021)
- [Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification](https://mlsanity.com/paper/2106.08808/) (2021)
- [DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis](https://mlsanity.com/paper/2204.10437/) (2022)
- [PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis](https://mlsanity.com/paper/2301.00772/) (2023)
- [Large-Scale 3D Medical Image Pre-training with Geometric Context Priors](https://mlsanity.com/paper/2410.09890/) (2024)
- [Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)](https://mlsanity.com/paper/2205.10342/) (2022)
- [Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder](https://mlsanity.com/paper/2306.08913/) (2023)
- [Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review](https://mlsanity.com/paper/2102.01530/) (2021)
- [MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis](https://mlsanity.com/paper/2404.15580/) (2024)
- [How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?](https://mlsanity.com/paper/2501.11253/) (2025)
- [CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning](https://mlsanity.com/paper/2504.13820/) (2025)
- [Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation](https://mlsanity.com/paper/2107.04886/) (2021)
- [Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes](https://mlsanity.com/paper/2403.16499/) (2024)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Attentive Symmetric Autoencoder for Brain MRI Segmentation](https://mlsanity.com/paper/2209.08887/) (2022)
- [MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet](https://mlsanity.com/paper/2206.00902/) (2022)
- [MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation](https://mlsanity.com/paper/2408.08070/) (2024)
- [Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images](https://mlsanity.com/paper/2309.07394/) (2023)
- [Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain](https://mlsanity.com/paper/2402.06190/) (2024)
- [Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network](https://mlsanity.com/paper/2411.13873/) (2024)
- [MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3D CT Lesions](https://mlsanity.com/paper/2108.07662/) (2021)
- [Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images](https://mlsanity.com/paper/2211.08643/) (2022)
- [Structure-aware Semantic Discrepancy and Consistency for 3D Medical Image Self-supervised Learning](https://mlsanity.com/paper/2507.02581/) (2025)
- [Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training](https://mlsanity.com/paper/2201.01426/) (2022)
- [Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images](https://mlsanity.com/paper/2205.15530/) (2022)
- [A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning](https://mlsanity.com/paper/2211.08533/) (2022)

## Abstract (arXiv)

Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learning is a potential solution to get rid of the strong requirement of training data by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical images. Specifically, we propose a context restoration task, i.e., Rubik's cube++, to pre-train 3D neural networks. Different from the existing context-restoration-based approaches, we adopt a volume-wise transformation for context permutation, which encourages network to better exploit the inherent 3D anatomical information of organs. Compared to the strategy of training from scratch, fine-tuning from the Rubik's cube++ pre-trained weight can achieve better performance in various tasks such as pancreas segmentation and brain tissue segmentation. The experimental results show that our self-supervised learning method can significantly improve the accuracy of 3D deep learning networks on volumetric medical datasets without the use of extra data.