一个模型搞定多器官分割，能处理标注不一致的数据。
MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets
- 用CNN+Transformer架构融合多尺度信息，解决标签冲突。
- 在不同视角和器官的腹部MRI数据上表现优于现有方法。
- 适合处理小规模、标注不统一的医学影像数据集。
多器官分割在临床任务中具有重要意义。实践中，相较于大规模全标注数据集，多个小规模数据集更易获取，但器官标注不一致。通常需为每个数据集单独训练模型，难以高效利用数据。由于标签冲突与数据不平衡，训练单一模型从多个部分标注数据集中稳健学习仍具挑战。本文提出MO-CTranS：一种可克服上述问题的统一模型。该模型采用基于CNN的编码器与基于Transformer的解码器，以多分辨率方式连接。解码器引入任务特定标记，以区分标签差异。我们在不同视角（轴向与冠状）及不同器官（肝、肾、脾）标注的腹部MRI数据集上评估并对比了多种基线与先进方法。结果表明，本方法在多数情况下性能更优（统计显著）。项目代码已开源：https://github.com/naisops/MO-CTranS。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。