PAFU-Net: A Pyramidal Attention-Fortified U-Net for Precise Liver Segmentation in Abdominal CT scans

The computer-aided diagnosis, surgical planning, treatment monitoring, and volumetric assessment of livers require proper liver segmentation by abdominal CT scans.Our architecture is PAFU-Net which is a pyramidal attention-strengthened U-Net architecture that incorporates Atrous Spatial Pyramid Pooling (ASPP) with a hybrid architecture of encoder channel attention, a bottleneck CBAM architecture, and decoder spatial attention.Moreover, ASPP-gated skip connections are used to add more multi-scale contextual representation and at the same time reducing background leakage and enhancing feature consistency within the network.Soft-tissue contrast and noise reduction prior to network inference are further improved by a lightweight preprocessing pipeline which includes slice elimination, HU-based windowing, and CLAHE.On the LiTS and 3D-IRCADb, PAFU-Net scores 0.9824 and 0.9277 on the Dice scores, respectively, and outperforms the standard U-Net and Attention U-Net models, as well as CBAM and ASPP-based ones, with much fewer parameters.The aggregate number of results shows high boundary preservation, excellent cross-dataset generalization and evident potential to be used in practical clinical settings in liver-segmentation processes.