# MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

arXiv: https://arxiv.org/abs/2212.14310 · HTML: https://mlsanity.com/paper/2212.14310/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 45 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 30 of 32)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images](https://mlsanity.com/paper/2007.10732/) (2020)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2203.01324/) (2022)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://mlsanity.com/paper/2006.16806/) (2020)
- [Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations](https://mlsanity.com/paper/1911.01218/) (2019)
- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning](https://mlsanity.com/paper/1812.00329/) (2018)
- [Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2005.09120/) (2020)
- [Abdominal multi-organ segmentation with organ-attention networks and statistical fusion](https://mlsanity.com/paper/1804.08414/) (2018)
- [Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training](https://mlsanity.com/paper/1804.02586/) (2018)
- [3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes](https://mlsanity.com/paper/1711.08580/) (2017)
- [An application of cascaded 3D fully convolutional networks for medical image segmentation](https://mlsanity.com/paper/1803.05431/) (2018)
- [Semi-Supervised Learning by Augmented Distribution Alignment](https://mlsanity.com/paper/1905.08171/) (2019)

## Built on by (most cited first, 22 of 22)

- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2311.11686/) (2023)
- [DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2504.04566/) (2025)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists](https://mlsanity.com/paper/2504.00862/) (2025)
- [Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.16625/) (2025)
- [Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution](https://mlsanity.com/paper/2404.16814/) (2024)
- [Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2508.13712/) (2025)
- [UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets](https://mlsanity.com/paper/2311.10251/) (2023)
- [Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/2501.13470/) (2025)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation](https://mlsanity.com/paper/2504.11856/) (2025)
- [P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.15861/) (2025)
- [Decoupled Competitive Framework for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.24667/) (2025)
- [C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2506.07368/) (2025)
- [JanusNet: Hierarchical Slice-Block Shuffle and Displacement for Semi-Supervised 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2508.03997/) (2025)
- [Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation](https://mlsanity.com/paper/2601.07377/) (2026)
- [Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2603.05202/) (2026)
- [SemiSAM-O1: Pushing the Boundary of Annotation-Efficient Medical Image Segmentation with Generalist Knowledge Fusion](https://mlsanity.com/paper/2604.24109/) (2026)
- [SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2607.07019/) (2026)
- [VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2607.07416/) (2026)
- [Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2607.25014/) (2026)

## Abstract (arXiv)

We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locations and variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N3 cubes cross- and within- labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and N3-cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with +7% DSC improvement on MACT dataset with 10% labeled images. Code is available at https://github.com/DeepMed-Lab-ECNU/MagicNet.