MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets

Multi-organ segmentation holds paramount significance in many clinical tasks. In practice, compared to large fully annotated datasets, multiple small datasets are often more accessible and organs are not labelled consistently. Normally, an individual model is trained for each of these datasets, which is not an effective way of using data for model learning. It remains challenging to train a single model that can robustly learn from several partially labelled datasets due to label conflict and data imbalance problems. We propose MO-CTranS: a single model that can overcome such problems. MO-CTranS contains a CNN-based encoder and a Transformer-based decoder, which are connected in a multi-resolution manner. Task-specific tokens are introduced in the decoder to help differentiate label discrepancies. Our method was evaluated and compared to several baseline models and state-of-the-art (SOTA) solutions on abdominal MRI datasets that were acquired in different views (i.e. axial and coronal) and annotated for different organs (i.e. liver, kidney, spleen). Our method achieved better performance (most were statistically significant) than the compared methods. Github link:this https URL.

@article{gong2025_2503.22557,
  title={ MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets },
  author={ Zhendi Gong and Susan Francis and Eleanor Cox and Stamatios N. Sotiropoulos and Dorothee P. Auer and Guoping Qiu and Andrew P. French and Xin Chen },
  journal={arXiv preprint arXiv:2503.22557},
  year={ 2025 }
}