BackgroundExisting methods for segmenting liver cancer from single-modal medical images fail to effectively leverage potential correlations across modalities. These correlations between the anatomical structures of liver cancer and the liver are also crucial for accurate liver cancer segmentation. These challenges not only limit the performance and scalability of liver cancer segmentation models but also pose significant challenges for researchers seeking to develop multimodal, low-annotation-dependent solutions. Therefore, it is necessary to propose a universal liver cancer segmentation network for abdominal computed tomography (CT) and magnetic resonance (MR) medical images.MethodBased on the above, we propose a two-stage universal liver cancer segmentation network for 3D dual-modality abdominal nuclear medical images using a mixed-label, multi-type training strategy. In stage 1, two CT and MR liver segmentation models are trained to generate liver mask images for CT and MR multimodal abdominal images without liver mask label images, thereby solving the laborious technical problem of liver labeling. In stage 2, a mixed-label strategy is proposed, where a mixed-label pool is constructed from CT and MR liver mask images generated by the aforementioned liver segmentation models, along with liver label images and their corresponding liver cancer label images. Subsequently, a universal liver cancer segmentation model is trained using a mixed-label, multi-type training strategy that fully considers potential correlations among different medical imaging modalities, liver cancer, and the liver.ResultsThe proposed liver cancer segmentation model, based on Medformer and the proposed mixed-label, multi-type abdominal-image training strategy, performs best, validating the effectiveness of the proposed strategy.DiscussionThe proposed two-stage universal liver cancer segmentation network, based on a mixed-label and multi-type training strategy, can effectively segment liver cancer in different 3D dual-modality abdominal CT and MR images, which may become an indispensable quantitative analysis tool for liver cancer in clinical practice.
Chen et al. (Wed,) studied this question.