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.

翻译：多器官分割在许多临床任务中具有至关重要的意义。实践中，相较于大型全标注数据集，多个小型数据集通常更易获取，且器官标注标准不一致。通常，每个数据集需单独训练模型，这并非利用数据进行模型学习的有效方式。由于标签冲突与数据不平衡问题，训练单一模型以稳健地从多个部分标注数据集中学习仍具挑战性。我们提出MO-CTranS：一种能够克服此类问题的单一模型。MO-CTranS包含基于CNN的编码器与基于Transformer的解码器，二者以多分辨率方式连接。解码器中引入任务特定标记以辅助区分标签差异。我们在不同切面（即轴位与冠状位）采集、标注不同器官（即肝脏、肾脏、脾脏）的腹部MRI数据集上评估本方法，并与多种基线模型及前沿解决方案进行比较。本方法取得了优于对比方法的性能（多数结果具有统计学显著性）。Github链接：https://github.com/naisops/MO-CTranS。