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External Anatomy of the Kidney

External Anatomy of the Kidney

The kidneys are a pair of bean-shaped organs in the human body that play a critical role in maintaining overall health. They filter out waste products from the blood, regulate blood pressure, maintain electrolyte balance, and stimulate the production of red blood cells.
The kidneys are located in the retroperitoneal space on either side of the vertebral column, protected posteriorly by the 11th and 12th ribs. The right kidney sits slightly lower than the left owing to the presence of the liver...

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Updated: Aug 3, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automatic Kidney Segmentation Method Based on an Enhanced Generative Adversarial Network.

Tian Shan^1,2,3, Yuhan Ying^1,2,3, Guoli Song^1,2

¹State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.

Diagnostics (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces SegTGAN, an automated kidney segmentation method using generative adversarial networks. SegTGAN significantly improves the accuracy of kidney tumor morphometry analysis from CT scans.

Keywords:

dense block generative adversarial networks kidney segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate kidney tumor morphometry is crucial for diagnosis and treatment planning.
Manual measurement of imaging variables for quantitative analysis is time-consuming and data-limited.

Purpose of the Study:

To develop an autonomous kidney segmentation technique, SegTGAN, to overcome limitations in quantitative analysis of kidney tumor morphology.
To improve the accuracy of CT-based kidney segmentation for better clinical outcomes.

Main Methods:

Proposed SegTGAN, a generative adversarial network-based autonomous kidney segmentation technique.
SegTGAN features a discriminator network with multi-scale feature extraction and a fully convolutional generator network with dense blocks.
Compared SegTGAN with U-Net, FCN, and SegAN using the Kits19 dataset.

Main Results:

SegTGAN achieved a Dice similarity coefficient (DSC) of 92.28%, volumetric overlap error (VOE) of 16.17%, accuracy (ACC) of 97.28%, and average surface distance (ASD) of 0.61 mm on the Kits19 dataset.
SegTGAN outperformed other evaluated neural networks in segmentation accuracy.
Demonstrated the potential of SegTGAN to enhance CT-based kidney segmentation.

Conclusions:

SegTGAN offers a promising solution for automated and accurate kidney segmentation.
The proposed model can aid in quantitative analysis of kidney tumor morphology, potentially improving clinical decision-making.
Further development of SegTGAN could advance precision in kidney cancer diagnosis and treatment.