Noisy hepatic vessel labels from Computer Tomography (CT) are popular due to ves- sels’ low-contrast and complex morphology. This is challenging for automatic hepatic vessel segmentation, which is essential to many hepatic surgeries such as liver resec- tion and transplantation. To exploit the noisy labeled data, we proposed a novel semi- supervised framework called dual consistency assisted multi-confident learning (DC- Multi-CL) for automatic hepatic vessel segmentation. The proposed framework con- tains a dual consistency architecture that learns not only the high-quality annotation data but also the low-quality data by boosting the prediction consistency on low-quality la- beled data robustly. Furthermore, we also present a multi-confident learning compo- nent to exploit the capability of global context information from multi-level network features and eradicate the human efforts on refining the low-quality data. Combining these ideas, we believe that it raises a potentially valuable approach to handle segmen- tation task, especially when the annotation data are noisy, e.g. unlabeled and misla- beled voxel-wise. Extensive experiments on two public datasets, i.e. 3DIRCADb and MSD8, demonstrate the effectiveness of each component and the superiority of the pro- posed method to other state-of-the-art methods in hepatic vessel segmentation and semi- supervised segmentation. The implementation of DC-Multi-CL is available at: https: //github.com/VinBrainJSC/DualConsistency_Mutil- CL.git.
