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Updated: Feb 23, 2026

Microdissection of Primary Renal Tissue Segments and Incorporation with Novel Scaffold-free Construct Technology

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Published on: March 27, 2018

CorteXpert: A model-based method for automatic renal cortex segmentation.

Dehui Xiang¹, Ulas Bagci², Chao Jin¹

¹School of Electronics and Information Engineering, Soochow University, Jiangsu 215006, China.

Medical Image Analysis

|September 10, 2017

Summary

This summary is machine-generated.

This study presents CorteXpert, an automated system for delineating kidney and renal cortex tissue in CT scans. It achieves high accuracy, improving medical image analysis for kidney diseases.

Keywords:

Cortex model adaptation Non-uniform graph search Renal cortex

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Published on: January 7, 2019

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Nephrology

Background:

Accurate delineation of kidney and renal cortex is crucial for diagnosing and monitoring kidney diseases.
Manual segmentation of kidney tissues from CT scans is time-consuming and prone to inter-observer variability.

Purpose of the Study:

To introduce CorteXpert, a novel model-based framework for fully automatic delineation of kidney and renal cortex tissue.
To evaluate the performance of CorteXpert on contrast-enhanced abdominal CT scans.

Main Methods:

A model-based approach incorporating cortex model adaptation and non-uniform graph search strategies.
Validation using a cross-validation strategy on a clinical dataset of 58 CT scans.

Main Results:

State-of-the-art segmentation accuracies achieved for kidney and renal cortex delineation.
Dice coefficients of 97.86% ± 2.41% for kidney and 97.48% ± 3.18% for renal cortex.

Conclusions:

CorteXpert demonstrates high accuracy and robustness in automatic kidney and renal cortex segmentation.
The proposed framework has the potential to significantly aid in clinical diagnosis and research related to kidney conditions.