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Gross Anatomy of the Liver

Gross Anatomy of the Liver

The liver, the largest gland within the human body, is a firm and reddish-brown organ. This wedge-shaped structure weighs approximately 1.5 kg and occupies a significant portion of the right hypochondriac and epigastric regions. It extends more to the right of the body's midline than to the left.
Located under the diaphragm, the liver is almost entirely ensconced within the rib cage, providing it with substantial protection. Except for the superior most bare area, the liver's surface is...

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Updated: Jul 29, 2025

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AC-E Network: Attentive Context-Enhanced Network for Liver Segmentation.

Yang Li, Beiji Zou, Peishan Dai

IEEE Journal of Biomedical and Health Informatics

|May 19, 2023

Summary

This summary is machine-generated.

This study introduces an Attentive Context-Enhanced Network (AC-E Network) for accurate liver segmentation in CT scans. The novel approach enhances 3D context extraction in 2D networks, improving precision without significantly increasing computational cost.

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Artificial Intelligence in Medicine

Background:

Accurate liver segmentation from CT scans is crucial for diagnosing and treating liver diseases.
Traditional 2D Convolutional Neural Networks (CNNs) overlook 3D spatial information, while 3D CNNs are computationally expensive and parameter-heavy.
Existing methods struggle to balance segmentation accuracy with computational efficiency.

Purpose of the Study:

To develop an efficient and accurate method for liver segmentation in CT images.
To address the limitations of 2D and 3D CNNs in liver segmentation by incorporating 3D context into a 2D framework.
To improve the accuracy of liver surface segmentation by focusing on both region and boundary delineation.

Main Methods:

Proposed an Attentive Context-Enhanced Network (AC-E Network) integrating an attentive context encoding module (ACEM) into a 2D backbone.
ACEM extracts 3D contextual information without a substantial increase in learnable parameters.
Implemented a dual segmentation branch with complementary loss to focus on both liver region and boundary segmentation.

Main Results:

The AC-E Network demonstrated superior performance compared to existing methods on the LiTS and 3D-IRCADb datasets.
Achieved a competitive equilibrium between segmentation precision and model parameter count, outperforming many state-of-the-art approaches.
The method effectively segments the liver region and its boundary with high accuracy.

Conclusions:

The AC-E Network offers an effective solution for liver segmentation in CT scans, overcoming the limitations of conventional CNNs.
This approach provides a computationally efficient yet highly accurate method for computer-aided liver disease diagnosis and treatment planning.
The proposed network advances the field of medical image analysis by enabling precise liver segmentation with optimized resource utilization.