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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Kidney segmentation from computed tomography images using deep neural network.

Luana Batista da Cruz1, José Denes Lima Araújo1, Jonnison Lima Ferreira1

  • 1Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Brazil.

Computers in Biology and Medicine
|August 10, 2020
PubMed
Summary

This study presents an automated method for kidney and kidney tumor segmentation in CT images using deep convolutional neural networks (CNNs) and image processing, achieving high accuracy and reducing false positives for improved clinical diagnosis.

Keywords:
Computed tomographyConvolutional neural networksKidney cancerKidney segmentationMedical images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Computational Pathology

Background:

  • Precise kidney and kidney tumor segmentation is crucial for clinical diagnosis and treatment planning.
  • Manual segmentation is time-consuming and suffers from inter-specialist variability.
  • Deep convolutional neural networks (CNNs) offer a promising computational approach for kidney segmentation.

Purpose of the Study:

  • To develop an automatic method for kidney segmentation in computed tomography (CT) images.
  • To minimize false positives in kidney segmentation using image processing techniques.
  • To assist in the early diagnosis of kidney tumors through accurate segmentation.

Main Methods:

  • Utilized the KiTS19 dataset for training and evaluation.
  • Employed AlexNet for scope reduction and U-Net 2D for initial kidney segmentation.
  • Implemented image processing techniques to reduce false positives by retaining the largest segmented elements (kidneys).

Main Results:

  • Achieved an average Dice coefficient of 96.33% and Jaccard index of 93.02% on 210 CTs.
  • Demonstrated high performance with an average sensitivity of 97.42% and specificity of 99.94%.
  • Obtained an average Dice coefficient of 93.03% in the KiTS19 challenge.

Conclusions:

  • Deep neural networks efficiently solve the kidney segmentation problem in CT images.
  • The proposed method achieves high precision in kidney segmentation.
  • Integration of image processing techniques effectively reduces false positives, enhancing clinical utility.