# Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

arXiv: https://arxiv.org/abs/1804.02586 · HTML: https://mlsanity.com/paper/1804.02586/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

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

- [Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.00673/) (2023)
- [Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer](https://mlsanity.com/paper/2112.04894/) (2021)
- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective](https://mlsanity.com/paper/2302.01735/) (2023)
- [SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.12591/) (2023)
- [Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels](https://mlsanity.com/paper/2107.13741/) (2021)
- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation](https://mlsanity.com/paper/2205.03644/) (2022)
- [Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation](https://mlsanity.com/paper/2206.02307/) (2022)
- [All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.13930/) (2021)
- [MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery](https://mlsanity.com/paper/2212.14310/) (2022)
- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2303.13090/) (2023)
- [Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization](https://mlsanity.com/paper/2103.04813/) (2021)
- [MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs](https://mlsanity.com/paper/2504.06897/) (2025)
- [Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations](https://mlsanity.com/paper/2110.11998/) (2021)
- [Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2304.07519/) (2023)
- [ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast](https://mlsanity.com/paper/2304.02689/) (2023)
- [SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2409.16728/) (2024)
- [Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction](https://mlsanity.com/paper/2105.14773/) (2021)
- [3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks](https://mlsanity.com/paper/2307.16256/) (2023)
- [Segmentation with mixed supervision: Confidence maximization helps knowledge distillation](https://mlsanity.com/paper/2109.10902/) (2021)
- [A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation](https://mlsanity.com/paper/2104.11020/) (2021)
- [Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean](https://mlsanity.com/paper/2108.04423/) (2021)
- [3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images](https://mlsanity.com/paper/2309.08481/) (2023)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2303.14175/) (2023)
- [DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference](https://mlsanity.com/paper/2404.16474/) (2024)
- [Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists](https://mlsanity.com/paper/2504.00862/) (2025)

## Abstract (arXiv)

In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain. In comparison, massive unlabeled 3D CT volumes are usually easily accessible. Current mainstream works to address the semi-supervised biomedical image segmentation problem are mostly graph-based. By contrast, deep network based semi-supervised learning methods have not drawn much attention in this field. In this work, we propose Deep Multi-Planar Co-Training (DMPCT), whose contributions can be divided into two folds: 1) The deep model is learned in a co-training style which can mine consensus information from multiple planes like the sagittal, coronal, and axial planes; 2) Multi-planar fusion is applied to generate more reliable pseudo-labels, which alleviates the errors occurring in the pseudo-labels and thus can help to train better segmentation networks. Experiments are done on our newly collected large dataset with 100 unlabeled cases as well as 210 labeled cases where 16 anatomical structures are manually annotated by four radiologists and confirmed by a senior expert. The results suggest that DMPCT significantly outperforms the fully supervised method by more than 4% especially when only a small set of annotations is used.