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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.
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Liver vessel segmentation based on extreme learning machine.

Ye Zhan Zeng¹, Yu Qian Zhao², Miao Liao¹

¹Department of Biomedical and Information Engineering, Central South University, Changsha 410083, China.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)

|May 2, 2016

Summary

This study introduces an effective extreme learning machine (ELM) method for liver-vessel segmentation in CT scans. The approach accurately identifies liver vessels, crucial for surgical planning.

Keywords:

CT ELM Liver vessels Segmentation

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Surgical Planning

Background:

Accurate liver-vessel segmentation is vital for liver surgical planning and vessel structure analysis.
Existing methods may face challenges with noise and complex vessel structures in CT images.

Purpose of the Study:

To develop and evaluate a novel liver-vessel segmentation method using extreme learning machine (ELM).
To enhance the accuracy and effectiveness of liver vessel segmentation in abdominal CT images.

Main Methods:

Anisotropic filtering for noise reduction while preserving vessel boundaries.
Application of multiple vessel filters (Sato, Frangi, offset medialness, strain energy) for feature extraction.
Segmentation using extreme learning machine (ELM) to differentiate vessels from background voxels.

Main Results:

The proposed ELM-based method effectively segments liver vessels from abdominal CT images.
Demonstrated good accuracy, sensitivity, and specificity in experimental results.
Successfully preserved vessel boundaries during the noise reduction process.

Conclusions:

The ELM-based liver-vessel segmentation method is effective for abdominal CT image analysis.
The method shows promise for improving liver surgical planning through accurate vessel visualization.
The combination of advanced filtering and ELM offers a robust solution for medical image segmentation.