MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets
This work addresses a practical problem in medical imaging for clinicians and researchers by enabling more efficient use of partially labeled data, though it is incremental as it builds on existing CNN and Transformer architectures.
The paper tackles the challenge of training a single multi-organ segmentation model from multiple small, heterogeneously labeled datasets, which often suffer from label conflicts and data imbalance, and reports that their proposed MO-CTranS model achieved better performance than baseline and state-of-the-art methods on abdominal MRI 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: https://github.com/naisops/MO-CTranS.