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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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

Syeda Sitara Waseem¹, Sarang Shaikh², Syed Rizwan Hassan³

¹Department of Computer Science & IT, The Government Sadiq College Women University, Bahawalpur 63100, Pakistan.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

This summary is machine-generated.

A new deep learning model, IMAU-Net, improves liver segmentation accuracy in laparoscopic surgery. This AI tool balances precision and speed for better intraoperative guidance systems.

Keywords:

Atrous Spatial Pyramid Pooling InceptionV3 Multi-Core Pooling deep learning laparoscopic surgery liver segmentation real-time segmentation

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Area of Science:

Medical Image Analysis
Surgical Technology
Artificial Intelligence in Medicine

Background:

Accurate liver segmentation is crucial in laparoscopic surgery but challenging due to visual complexities.
Existing deep learning models struggle to balance segmentation accuracy, computational efficiency, and boundary precision.

Purpose of the Study:

To develop and evaluate IMAU-Net, a novel hybrid deep learning architecture for precise and efficient liver segmentation in laparoscopic surgery.
To address limitations of current models by integrating advanced feature extraction techniques.

Main Methods:

Proposed IMAU-Net architecture: a hybrid model combining a pre-trained InceptionV3 encoder with a novel bottleneck.
The bottleneck integrates Multi-Core Pooling (MCP) for fine-to-medium spatial details and enhanced Atrous Spatial Pyramid Pooling (ASPP) for multi-scale context.
Evaluated using 5-fold cross-validation on the M2CAI dataset and external validation on the CholecSeg8K dataset.

Main Results:

IMAU-Net achieved a mean Dice coefficient of 0.9179 ± 0.012 and IoU of 0.8483 ± 0.015 on the M2CAI dataset.
External validation on CholecSeg8K dataset yielded a Dice coefficient of 0.8745 ± 0.0312 and AUC of 0.9542, showing generalizability.
The model demonstrates superior performance compared to state-of-the-art methods with high computational efficiency (45 FPS, 42.3 M parameters).

Conclusions:

IMAU-Net offers an optimal balance between accuracy and efficiency for liver segmentation in laparoscopic surgery.
The model shows potential for integration into real-time intraoperative guidance systems.
Further prospective validation is recommended for real-time intraoperative workflow integration.