Reciprocal cross-modal guidance for liver lesion segmentation from multiple phases under incomplete overlap

Abstract

Objective:Liver lesion segmentation is beneficial for computer-aided disease diagnosis, which can be further enhanced by incorporating multi-phase images. Nevertheless, existing segmentation models still need improvement as they build on the unsolid assumption that lesions are well-aligned between different phases. Our goal was to develop a robust model that achieves high accuracy in lesion segmentation, even in cases where incomplete overlap occurs across multiple phases. Methods:To overcome the strong assumption of precise alignment between phases, we introduce a novel deep neural network featuring multi-scale feature fusion and cross-phase feature guidance. The former enables the retrieval of effective information from each phase that is helpful in lesion segmentation, while the latter ensures exchanging of guidance maps for interphase segmentation. Results:Comprehensive experiments were carried out on an in-house multi-phase dataset having liver lesions, as well as a multi-modal dataset from the CHAOS challenge. Results show that our model surpasses existing state-of-the-art methods in segmenting liver lesions and abdominal organs from multi-phase images, even if the registration between phases is inaccurate. Conclusion:A competent solution was proposed for accurate liver lesion segmentation from multi-phase images, considering the imperfect registration between phases. The model was proved to be effective in extracting pertinent information from each phase and sharing useful information between phases. Significance:The accommodation of incomplete overlap in multi-phase images provides a new paradigm of accurate and robust lesion segmentation, which is particularly helpful in real-world scenarios where registration accuracy may be compromised.