Multi-organ Segmentation with Partially Annotated Datasets

Zusammenfassung

Efficient and fully automatic multi-organ segmentation is of great research and clinical prospect. Deep learning (DL) based methods have recently emerged and proven its effectiveness in various biomedical segmentation tasks. The performance of DL based segmentation models strongly depends on the training dataset and a large, correctly annotated dataset is always crucial. However, gathering annotation for multi-organ segmentation task is difficult and making use of public datasets with existing annotations then becomes one possible solution. In this work we propose a pipeline for training multi-organ segmentation model from partially annotated datasets. The proposed method is evaluated using left, right lungs and liver segmentation task of throat-abdomen CT scans. From average dice score, we found the proposed method can obtain very close performance using only partially annotated datasets (0.93), compared with models using fully annotated datasets (0.96).