Effective registration-free dual-phase segmentation for pancreas and pancreatic mass via symmetrical selective feature integration.

Abstract

Pancreatic masses present significant challenges in clinical management due to their diverse manifestations and inherent complexity. Dual-phase contrast-enhanced CT is essential for accurate diagnosis, yet widely adopted segmentation methods rely on image registration, which compromises both precision and efficiency. In this study, we introduce a novel architecture that utilizes a cross-attention mechanism for selective feature integration across different phases, achieving registration-free dual-phase segmentation of the pancreas and pancreatic masses. Our model incorporates a dual-path encoder with symmetrical branches specifically designed for the arterial and portal venous phases, where weight-shared cross-attention modules perform symmetrical feature selection and alignment, obviating explicit registration. We further design a progressive fusion decoder that incrementally merges features from both branches through multiple cross-attention modules, ensuring optimal utilization of information from both imaging phases throughout the decoding process. Extensive evaluations on one internal and three external datasets demonstrate that our approach not only outperforms previous registration-dependent methods in accuracy (Dice: 81.86% vs 76.68%) but also improves inference speeds (10.55s vs 130.07s per scan), setting new benchmarks in the field. Additional comparative experiments underscore the efficacy and robustness of our symmetrical fusion framework, confirming its potential as a superior alternative to conventional techniques.