IMAU-Net: A Hybrid Multi-Scale Deep Learning Framework for Liver Segmentation from Laparoscopic Images

Accurate liver segmentation in laparoscopic surgery is critical but remains challenging due to low contrast, occlusion, and irregular organ boundaries. While deep learning has advanced medical image segmentation, existing models often trade off between accuracy, computational efficiency, and boundary precision. We propose IMAU-Net, a hybrid architecture integrating a pre-trained InceptionV3 encoder with a novel bottleneck combining Multi-Core Pooling (MCP) and enhanced Atrous Spatial Pyramid Pooling (ASPP). The MCP module captures fine-to-medium spatial details through parallel multi-kernel pooling, while ASPP extracts multi-scale contextual information via dilated convolutions. Evaluated on the M2CAI dataset with 5-fold cross-validation, IMAU-Net achieves a mean Dice coefficient of 0.9179 ± 0.012 and IoU of 0.8483 ± 0.015. Furthermore, external validation on the independent CholecSeg8K dataset (250 test samples) demonstrates generalizability across different laparoscopic procedures, achieving a Dice coefficient of 0.8745 ± 0.0312 and AUC of 0.9542, with a performance degradation of only 4.3% despite domain shift between liver surgery and cholecystectomy. Comparative analysis with state of the art methods demonstrates superior performance, with computational efficiency suitable for real-time applications (45 FPS, 42.3 M parameters). The proposed architecture provides an optimal balance between accuracy and efficiency for intraoperative guidance systems. While evaluated on retrospective laparoscopic image datasets rather than real-time intraoperative workflows, the model demonstrates potential for integration into surgical guidance systems pending prospective validation.