Development and evaluation of automated ultrasonographic detection of bladder diameter for estimation of bladder urine volume

Matsumoto, Masaru; Tsutaoka, Takuya; Yabunaka, Koichi; Handa, Mayumi; Yoshida, Mikako; Nakagami, Gojiro; Sanada, Hiromi

doi:10.1371/journal.pone.0219916

Cited by 32 publications

(24 citation statements)

References 15 publications

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“…However, the analysis showed that a high contrast is crucial to prevent segmentation artifacts. This is underlined by the comparison with earlier work on the use of CNNs for bladder segmentation from ultrasound data [26,27], where a higher correlation between the automatically determined and the manually segmented bladder volumes was obtained. However, it should be mentioned, that the cited work was based on the use of high-end clinical ultrasound devices, which provide higher contrast, and two orthogonal B-mode images were acquired for obtaining quantitative values for the bladder volume [26].…”

Section: Discussionmentioning

confidence: 58%

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation

Fournelle

Grün

Speicher

et al. 2021

Sensors

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We developed a new mobile ultrasound device for long-term and automated bladder monitoring without user interaction consisting of 32 transmit and receive electronics as well as a 32-element phased array 3 MHz transducer. The device architecture is based on data digitization and rapid transfer to a consumer electronics device (e.g., a tablet) for signal reconstruction (e.g., by means of plane wave compounding algorithms) and further image processing. All reconstruction algorithms are implemented in the GPU, allowing real-time reconstruction and imaging. The system and the beamforming algorithms were evaluated with respect to the imaging performance on standard sonographical phantoms (CIRS multipurpose ultrasound phantom) by analyzing the resolution, the SNR and the CNR. Furthermore, ML-based segmentation algorithms were developed and assessed with respect to their ability to reliably segment human bladders with different filling levels. A corresponding CNN was trained with 253 B-mode data sets and 20 B-mode images were evaluated. The quantitative and qualitative results of the bladder segmentation are presented and compared to the ground truth obtained by manual segmentation.

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Section: Discussionmentioning

confidence: 58%

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation

Fournelle

Grün

Speicher

et al. 2021

Sensors

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“…Each dimension is the maximum diameter derived from the transverse and longitudinal views using 2-D ultrasound imaging equipment. The volume estimation method of the bladder is mainly recommended as an ellipsoid because of its relatively accurate results and ease of application [ 28 , 29 ]. Among the shapes assumed as the bladder, the ellipsoid can be derived by a single quadric surface equation.…”

Section: Methodsmentioning

confidence: 99%

Forward-Looking Ultrasound Wearable Scanner System for Estimation of Urinary Bladder Volume

Park

Joung

et al. 2021

Sensors

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Accurate measurement of bladder volume is an important tool for evaluating bladder function. In this study, we propose a wearable bladder scanner system that can continuously measure bladder volume in daily life for urinary patients who need urodynamic studies. The system consisted of a 2-D array, which included integrated forward-looking piezoelectric transducers with thin substrates. This study aims to estimate the volume of the bladder using a small number of piezoelectric transducers. A least-squares method was implemented to optimize an ellipsoid in a quadratic surface equation for bladder volume estimation. Ex-vivo experiments of a pig bladder were conducted to validate the proposed system. This work presents the potential of the approach for wearable bladder monitoring, which has similar measurement accuracy compared to the commercial bladder imaging system. The wearable bladder scanner can be improved further as electronic voiding diaries by adding a few more features to the current function.

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“…The number of channels in each convolutional layer of the VGG16‐like CNN was one‐fourth of that of the VGG16. The above procedure was performed with reference to a previous study (Matsumoto et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Deep learning‐based classification of rectal fecal retention and analysis of fecal properties using ultrasound images in older adult patients

Matsumoto

Tsutaoka

Nakagami

et al. 2020

Japan Journal Nursing Sci

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Aim: The present study aimed to analyze the use of machine learning in ultrasound (US)-based fecal retention assessment. Methods: The accuracy of deep learning techniques and conventional US methods for the evaluation of fecal properties was compared. The presence or absence of rectal feces was analyzed in 42 patients. Eleven patients without rectal fecal retention on US images were excluded from the analysis; thus, fecal properties were analyzed in 31 patients. Deep learning was used to classify the transverse US images into three types: absence of feces, hyperechoic area, and strong hyperechoic area in the rectum. Results: Of the 42 patients, 31 tested positive for the presence of rectal feces, zero were false positive, zero were false negative, and 11 were negative, indicating a sensitivity of 100% and a specificity of 100% for the detection of rectal feces in the rectum. Of the 31 positive patients, 14 had hard stools and 17 had other types. Hard stool was detected by US findings in 100% of the patients (14/14), whereas deep learning-based classification detected hard stool in 85.7% of the patients (12/14). Other stool types were detected by US findings in 88.2% of the patients (15/17), while deep learning-based classification also detected other stool types in 88.2% of the patients (15/17). Conclusions: The results showed that US findings and deep learning-based classification can detect rectal fecal retention in older adult patients and distinguish between the types of fecal retention.

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Development and evaluation of automated ultrasonographic detection of bladder diameter for estimation of bladder urine volume

Cited by 32 publications

References 15 publications

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation

Forward-Looking Ultrasound Wearable Scanner System for Estimation of Urinary Bladder Volume

Deep learning‐based classification of rectal fecal retention and analysis of fecal properties using ultrasound images in older adult patients

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