Liver CT image segmentation network based on multi-scale feature fusion

Accurate localization and segmentation of the liver based on CT images are crucial for the early diagnosis and staging of liver cancer. However, during CT scanning, the liver's position may vary due to periodic respiratory motion, and its boundaries are often unclear due to its close proximity to surrounding organs, which increases the difficulty of segmentation. To address these challenges, this paper proposes an end-to-end liver image segmentation network-EDNe, based on a residual structure. The model introduces an automated feature fusion module (ECAdd) and a residual structure, enhancing the network's ability to extract multi-scale features from liver CT images. Additionally, a Deep Feature Enhancement (DFE) attention module is incorporated during the decoding phase to improve the network's ability to capture fine-grained details, thereby ensuring an effective improvement in segmentation accuracy. EDNet was validated on the LiTS2017 and 3D-IRCADb-01 datasets, achieving Dice scores of 0.9651 and 0.9683, and IoU scores of 0.9330 and 0.9385 on the LiTS2017 and 3D-IRCADb-01 datasets, respectively. Experimental results show that EDNet not only exhibits significant advantages in segmentation performance but also demonstrates high robustness across different datasets, providing a reliable and effective solution for liver CT image segmentation tasks.