ArticleSensors (Basel, Switzerland)2020
Fully Automated Segmentation of Bladder Sac and Measurement of Detrusor Wall Thickness from Transabdominal Ultrasound Images.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Improved bladder diagnostics using multiparametric ultrasound.Abdominal radiology (New York) · 2025Review
- Pre-hospital Identification of a Giant Bladder Calculus through Screening Sonography: A Case Report.Current medical imaging · 2025Article
- Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023Review
- State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Classification of Monocytes, Promonocytes and Monoblasts Using Deep Neural Network Models: An Area of Unmet Need in Diagnostic Hematopathology.Journal of clinical medicine · 2021Article
- Non-invasive assessment of urinary bladder compliance using ultrasound: first validation study based on clinical urodynamic study.Annals of translational medicine · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Ultrasound measurements of detrusor muscle thickness have been proposed as a diagnostic biomarker in patients with bladder overactivity and voiding dysfunction. In this study, we present an approach based on deep learning (DL) and dynamic programming (DP) to segment the bladder sac and measure the detrusor muscle thickness from transabdominal 2D B-mode ultrasound images. To assess the performance of our method, we compared the results of automated methods to the manually obtained reference bladder segmentations and wall thickness measurements of 80 images obtained from 11 volunteers. It takes less than a second to segment the bladder from a 2D B-mode image for the DL method. The average Dice index for the bladder segmentation is 0.93 ± 0.04 mm, and the average root-mean-square-error and standard deviation for wall thickness measurement are 0.7 ± 0.2 mm, which is comparable to the manual ground truth. The proposed fully automated and fast method could be a useful tool for segmentation and wall thickness measurement of the bladder from transabdominal B-mode images. The computation speed and accuracy of the proposed method will enable adaptive adjustment of the ultrasound focus point, and continuous assessment of the bladder wall during the filling and voiding process of the bladder.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.