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Updated: Jan 27, 2026

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Kidney cortex segmentation in 2D CT with U-Nets ensemble aggregation.

V Couteaux1, S Si-Mohamed2, R Renard-Penna3

  • 1Philips Research France, 33, rue de Verdun, 92150 Suresnes, France; LTCI, Télécom ParisTech, Université Paris-Saclay, 75013 Paris, France.

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Summary
This summary is machine-generated.

This study developed an accurate kidney cortex segmentation method using deep learning on CT images, achieving a 0.867 Dice score and ranking first in a radiology challenge.

Keywords:
Artificial intelligence (AI)Computed tomography (CT)Image segmentationRenal cortex

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Accurate segmentation of renal structures is crucial for diagnosing kidney diseases.
  • Computed tomography (CT) is a key imaging modality for renal assessment.

Purpose of the Study:

  • To develop and evaluate an automated method for segmenting the kidney cortex from coronal CT images.
  • To participate and achieve high performance in a French Radiology Society data challenge.

Main Methods:

  • Ensemble of fully-convolutional networks were trained for segmentation.
  • Predictions from multiple networks were aggregated to improve accuracy.
  • A dataset of 497 CT images with segmentation masks was utilized for training and validation.

Main Results:

  • The proposed algorithm achieved precise segmentation of the renal cortex.
  • A Dice score of 0.867 was obtained, ranking the method first in the challenge.
  • The results demonstrate the robustness of the deep learning approach.

Conclusions:

  • The developed solution offers accurate and robust automatic renal cortex segmentation in CT images.
  • The precision of reference segmentations may have limited the maximum achievable performance.
  • Future work should explore 3D segmentation for renal cortex volume quantification, requiring significant labeling efforts.