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Gross Anatomy of the Liver

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

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

Published on: November 30, 2022

Deeply self-supervised contour embedded neural network applied to liver segmentation.

Minyoung Chung¹, Jingyu Lee¹, Minkyung Lee¹

¹School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.

Computer Methods and Programs in Biomedicine

|March 24, 2020

Summary

This summary is machine-generated.

This study introduces a novel neural network for liver segmentation in abdominal CT scans. The AI model, guided by contour features, achieved a 2.13% higher dice score than existing methods, improving liver segmentation accuracy.

Keywords:

Contour embedded network Convolutional neural network Liver segmentation Self-supervising network

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate liver segmentation is crucial for medical diagnosis and treatment planning.
Existing segmentation methods often struggle with complex volumetric data from CT scans.

Purpose of the Study:

To develop and evaluate a neural network-based algorithm for precise liver segmentation using abdominal CT images.
To enhance segmentation accuracy by incorporating complementary contour features.

Main Methods:

A fully convolutional network was designed for volumetric image segmentation.
An adaptive self-supervision scheme was employed for deep supervision, deriving essential contour features.
Discriminative contour, shape, and deep features were merged for improved segmentation.

Main Results:

The proposed algorithm was trained and validated on 160 abdominal CT images.
Quantitative evaluation using eight-fold cross-validation demonstrated superior performance.
The method incorporating contour features achieved a 2.13% improvement in the dice score compared to state-of-the-art techniques.

Conclusions:

A novel framework was introduced to guide neural networks in learning complementary contour features.
The proposed neural network significantly enhances liver segmentation performance.
Guided contour features are key to improving the accuracy of medical image segmentation tasks.