Multi-phase and Multi-level Selective Feature Fusion for Automated Pancreas Segmentation from CT Images

Abstract

CT images scanned in arterial and venous phases have been demonstrated to provide complementary information for accurate pancreas segmentation. In this paper, we propose a novel multi-phase and multi-level selective feature fusion network (MMNet) with a core component named adaptive cross refinement (ACR) module. Specifically, MMNet adopts two parallel encoders to extract features of the two phases respectively, which are then fused by ACR to excel each complementarity advantage. Unlike most existing fusion methods which only exchange and combine features of a single level with the same resolution between two phases/modalities, ACR module intelligently aggregates features of all levels in one phase as a multi-level prior, and then adaptively selects the most effective information from the multi-level prior to refine features at each level of the other phase. Such multi-phase, multi-level selective feature exchange and fusion strategy is bi-directional to mutually benefit segmentation of both phases. Experimental results on 141 cases of our private dataset demonstrate the effectiveness of our ACR module and superior performance to the state-of-the-art fusion methods.