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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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Related Experiment Video

Updated: Mar 8, 2026

Rat Model of the Associating Liver Partition and Portal Vein Ligation for Staged Hepatectomy ALPPS Procedure

Rat Model of the Associating Liver Partition and Portal Vein Ligation for Staged Hepatectomy ALPPS Procedure

Published on: August 14, 2017

Probabilistic liver atlas construction.

Esther Dura¹, Juan Domingo¹, Guillermo Ayala²

¹Department of Informatics, School of Engineering, University of Valencia, Avda. de la Universidad, 46100, Burjasot, Spain.

Biomedical Engineering Online

|January 15, 2017

Summary

This summary is machine-generated.

A new generalized linear model improves liver probabilistic atlas construction. Flexible registration methods can negatively impact atlas adaptability and segmentation accuracy in new cases.

Keywords:

Anatomical atlas Atlas variability Coregistration method Generalized linear model Probabilistic atlas

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Last Updated: Mar 8, 2026

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Published on: September 28, 2021

Area of Science:

Medical image analysis
Computational anatomy

Background:

Anatomical atlases are 3D representations of human organs.
Probabilistic atlases estimate object probability at each spatial location.
Current methods often use simple estimations.

Purpose of the Study:

To introduce a novel method for probabilistic atlas construction using a generalized linear model.
To enhance the accuracy of liver probability estimation.
To investigate the impact of coregistration on atlas quality.

Main Methods:

Developed a new probabilistic atlas construction method based on a generalized linear model.
Evaluated the influence of geometrical transformations in the coregistration step.
Assessed the adaptability of atlases to new cases.

Main Results:

The proposed generalized linear model method demonstrated good performance.
Certain coregistration methods significantly affected atlas construction quality.
Highly flexible registration can reduce atlas variability and segmentation aid.

Conclusions:

The new approach shows effective performance in probabilistic atlas construction.
Overly flexible registration may not always be beneficial for atlas adaptability.
Atlas adaptability is crucial for accurate segmentation in new cases.