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Automatic renal cortex segmentation using implicit shape registration and novel multiple surfaces graph search.

Xiuli Li¹, Xinjian Chen, Jianhua Yao

¹Intelligent Medical Research Center, Institute of Automation, Chinese Academy of Science, Beijing 100190, China.

IEEE Transactions on Medical Imaging

|June 15, 2012

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This study introduces an automated method for segmenting the renal cortex using implicit shape registration and graph search. The novel approach achieves high accuracy in segmenting kidney structures from CT scans.

Area of Science:

Medical Imaging
Computational Anatomy
Biomedical Engineering

Background:

Accurate segmentation of the renal cortex is crucial for diagnosing and monitoring kidney diseases.
Existing segmentation methods often struggle with complex anatomical variations and image noise.

Purpose of the Study:

To develop and validate an automatic renal cortex segmentation approach.
To improve the accuracy and robustness of renal cortex segmentation in computed tomography (CT) scans.

Main Methods:

A hierarchical approach combining implicit shape registration and multiple surfaces graph search.
Initialization of the whole kidney using implicit shape registration within Euclidean distance functions.
Extraction of renal cortex surfaces via graph searching with adaptable sampling and constraints, followed by a refining step to minimize errors around the renal pelvis.

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Main Results:

The method achieved high performance on 17 clinical CT scans.
Key metrics included Dice similarity coefficient (DSC) of 90.50% ± 1.19%, volumetric overlap error (OE) of 4.38% ± 3.93%, signed relative volume difference (SVD) of 2.37% ± 1.72%, average symmetric surface distance (D(avg)) of 0.14 mm ± 0.09 mm, and average symmetric rms surface distance (D(rms)) of 0.80 mm ± 0.64 mm.

Conclusions:

The proposed automatic renal cortex segmentation method demonstrates feasibility, efficiency, and robustness.
The technique offers a reliable tool for quantitative analysis of kidney structures in medical imaging.