Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

492
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...
492
Computed Tomography01:10

Computed Tomography

9.1K
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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
9.1K
Imaging Studies VI: Voiding Cystourethrography and Cystography01:22

Imaging Studies VI: Voiding Cystourethrography and Cystography

2.6K
Voiding Cystourethrography (VCUG) and Cystography are specialized radiographic procedures used to examine the structure and function of the bladder and urethra.Voiding Cystourethrography (VCUG)A Voiding Cystourethrogram (VCUG) is a diagnostic imaging procedure that assesses the anatomy and function of the lower urinary tract. It focuses on the bladder, bladder neck, and urethra, helping detect abnormalities such as vesicoureteral reflux (VUR)—the backward or reverse flow of urine into the...
2.6K
Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

597
IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
597
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

489
Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
489
Imaging Studies V: Intravenous Urography and Retrograde Pyelography01:22

Imaging Studies V: Intravenous Urography and Retrograde Pyelography

2.0K
IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
2.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Electrocardiogram lead conversion from single-lead blindly-segmented signals.

BMC medical informatics and decision making·2022
Same author

Finite element analysis of the effect of high tibial osteotomy correction angle on articular cartilage loading.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2018
Same author

Segmentation of tongue shapes during vowel production in magnetic resonance images based on statistical modelling.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2018
Same author

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel.

Sensors (Basel, Switzerland)·2017

Related Experiment Video

Updated: Feb 28, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.7K

A versatile method for bladder segmentation in computed tomography two-dimensional images under adverse conditions.

João Ribeiro Pinto1, João Manuel Rs Tavares2

  • 11 Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|June 20, 2017
PubMed
Summary

This study introduces a novel semi-automatic algorithm for segmenting urinary bladders in CT scans, aiding in early bladder cancer detection. The method shows promise for computer-aided diagnosis, particularly for small tumors.

Keywords:
Medical imagingactive contourscancerfuzzy c-meansimage segmentationurinary bladder

More Related Videos

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing
05:25

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

Published on: August 14, 2019

20.4K
Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.7K

Related Experiment Videos

Last Updated: Feb 28, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.7K
Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing
05:25

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

Published on: August 14, 2019

20.4K
Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.7K

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Oncology

Background:

  • Accurate urinary bladder segmentation is crucial for diagnosing bladder wall tumors.
  • Tumor-induced structural changes complicate segmentation in contrastless CT scans.
  • Existing methods may lack adaptability to tumor-related image irregularities.

Purpose of the Study:

  • To design and evaluate a versatile semi-automatic algorithm for urinary bladder segmentation.
  • To address segmentation challenges posed by bladder wall tumors in CT imaging.
  • To assess the algorithm's efficacy for early-stage bladder cancer diagnosis.

Main Methods:

  • A semi-automatic segmentation approach combining fuzzy c-means clustering, Gaussian-curve intensity transformation, and active contour models.
  • Requires minimal physician input (a single seed point per view).
  • Evaluated on contrastless CT images from The Cancer Genome Atlas-Urothelial Bladder Carcinoma collection.

Main Results:

  • Achieved approximately 79% successful segmentation for small tumors (<2.0 cm).
  • Achieved approximately 72% successful segmentation for medium tumors (2.0-2.9 cm).
  • For small tumors, achieved an average Hausdorff distance of 3.7 mm and 91.0% overlap.

Conclusions:

  • The developed algorithm demonstrates high potential for effective urinary bladder segmentation.
  • The method shows particular promise for computer-aided diagnosis of early-stage bladder wall tumors.
  • Its adaptability makes it suitable for challenging cases with tumor-induced structural changes.