MLAGG-Net: Multi-level aggregation and global guidance network for pancreatic lesion segmentation in histopathological images

Abstract

Pancreatic cancer is one of the most aggressive and lethal malignancies in the world. Automatic and accurate segmentation of lesion regions from histopathological images is essential for disease diagnosis and analysis. Nevertheless, it remains a challenging task due to the complicated pathological manifestations of the lesion, including the ambiguity of boundaries, the heterogeneity of textures, and the large variations in morphology, size and location. To address these problems, we propose a multi-level aggregation and global guidance network. Specifically, we utilize a multi-level feature fusion and distribution module that aims to alleviate the scale diversity and complex boundary problems in lesions by aggregating fine-grained information of low-level features and semantic information of high-level features. The cross-attention fusion module is proposed to perform the aggregation of hierarchical multi-level features, which can absorb effective information and suppress redundant information from them. This paper also introduces a global information fusion module to guide the network to locate the lesion areas with inconsistent distribution between the internal and boundary regions more accurately for more complete segmentation results. We conduct experiments on our PANC dataset and the public GlaS challenge dataset to verify the effectiveness of the proposed network. The experimental results show that the proposed method can achieve better segmentation performance with 90.02% Dice and 82.07% Jaccard compared to other state-of-the-art methods on our PANC dataset while achieving competitive results on the GlaS challenge dataset.