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

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Published on: November 30, 2022

Two-stream MeshCNN for key anatomical segmentation on the liver surface.

Xukun Zhang¹, Sharib Ali², Minghao Han¹

¹Academy for Engineering and Technology, Fudan University, Shanghai, 200082, China.

International Journal of Computer Assisted Radiology and Surgery

|June 10, 2025

Summary

A new two-stream mesh convolutional network (TSMCN) accurately segments liver surfaces by combining global and local information. This method improves anatomical region segmentation for enhanced augmented reality-guided liver surgery.

Keywords:

AR-guided surgery Landmark segmentation Liver surface MeshCNN

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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 and computer-assisted surgery
3D mesh processing and deep learning

Background:

Accurate liver surface segmentation is crucial for intraoperative navigation.
Current methods struggle with liver shape variations and limited data.

Purpose of the Study:

Develop a two-stream mesh convolutional network (TSMCN) for automatic liver segmentation.
Integrate global geometric and local topological information for improved accuracy.

Main Methods:

Proposed TSMCN with two streams: E-stream for edge topology, P-stream for coordinate spatial relationships.
Adaptive fusion using a fine-grained aggregation (FGA) attention mechanism.
Generation of a pooled mesh preserving task-relevant structures.

Main Results:

TSMCN outperformed PointNet++ and MeshCNN on 200 3D liver mesh datasets.
Achieved superior segmentation of liver ridge and falciform ligament.
Significantly reduced 3D Chamfer distance, especially for falciform ligament.

Conclusions:

TSMCN effectively segments liver surfaces by integrating complementary geometric features.
Demonstrated potential for enhancing AR-guided liver surgery through precise preoperative segmentation.