# Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets

arXiv: https://arxiv.org/abs/2008.07149 · HTML: https://mlsanity.com/paper/2008.07149/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 26 of 26)

- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation](https://mlsanity.com/paper/2303.14444/) (2023)
- [Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation](https://mlsanity.com/paper/2306.02416/) (2023)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training](https://mlsanity.com/paper/2304.14030/) (2023)
- [Show and Segment: Universal Medical Image Segmentation via In-Context Learning](https://mlsanity.com/paper/2503.19359/) (2025)
- [Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation](https://mlsanity.com/paper/2311.10696/) (2023)
- [MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets](https://mlsanity.com/paper/2108.02559/) (2021)
- [MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model](https://mlsanity.com/paper/2410.05905/) (2024)
- [ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data](https://mlsanity.com/paper/2308.04070/) (2023)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Multi-site Organ Segmentation with Federated Partial Supervision and Site Adaptation](https://mlsanity.com/paper/2302.03911/) (2023)
- [Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation](https://mlsanity.com/paper/2407.12611/) (2024)
- [Uncertainty-aware Incremental Learning for Multi-organ Segmentation](https://mlsanity.com/paper/2103.05227/) (2021)
- [Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation](https://mlsanity.com/paper/2409.03228/) (2024)
- [External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data](https://mlsanity.com/paper/2201.00942/) (2022)
- [SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations](https://mlsanity.com/paper/2301.07074/) (2023)
- [Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation](https://mlsanity.com/paper/2503.05190/) (2025)
- [MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets](https://mlsanity.com/paper/2503.22557/) (2025)
- [CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2505.18958/) (2025)
- [Exploiting Unlabeled Structures through Task Consistency Training for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2509.04732/) (2025)
- [Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation](https://mlsanity.com/paper/2609.19176/) (2026)
- [FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation](https://mlsanity.com/paper/2609.24627/) (2026)

## Abstract (arXiv)

Multi-organ segmentation has extensive applications in many clinical applications. To segment multiple organs of interest, it is generally quite difficult to collect full annotations of all the organs on the same images, as some medical centers might only annotate a portion of the organs due to their own clinical practice. In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the the other organs from another set of training images. Existing approaches mostly train and deploy a single model for each subset of organs, which are memory intensive and also time inefficient. In this paper, we propose to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets. We collaboratively train two networks and let the coupled networks teach each other on un-annotated organs. To alleviate the noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative teaching, which further boosts the performance. Extensive experiments on three public available single-organ datasets LiTS, KiTS, Pancreas and manually-constructed single-organ datasets from MOBA show that our method can better utilize the few-organ datasets and achieves superior performance with less inference computational cost.