A Lightweight Network for Contextual and Morphological Awareness for Hepatic Vein Segmentation.

Accurate segmentation of the hepatic vein is crucial for enhancing the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. The experimental results on the public dataset show that when using the proposed network structure, it only needs less than 1/14 of the parameters of U-Net to outperform other comparison methods. Meanwhile, compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient, achieving an improvement of 2.86% and 1.45% on the two datasets, respectively. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.