Novel Three-Dimensional Bladder Reconstruction Model from B-Mode Ultrasound Image to Improve the Accuracy of Bladder Volume Measurement

Chang, Meng-Lin; Li, Hsiao-Chi; Liu, Chang-Keng; Chiang, Han-Sun; Hsu, Chien-Chang

doi:10.3390/s21144893

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…But this method requires sophisticated 3D technology and software to implement. 23 Further, Chang et al 25 developed a novel 3D reconstruction model from B-mode (2D) ultrasound images for bladder volume measurement. The super ellipse shape modification was used in their 3D reconstruction model.…”

Section: Costeffectivenessmentioning

confidence: 99%

Weakly supervised learning‐based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement

et al. 2023

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BackgroundAccurate measurement of bladder volume is necessary to maintain the consistency of the patient's anatomy in radiation therapy for pelvic tumors. As the diversity of the bladder shape, traditional methods for bladder volume measurement from 2D ultrasound have been found to produce inaccurate results.PurposeTo improve the accuracy of bladder volume measurement from 2D ultrasound images for patients with pelvic tumors.MethodsThe bladder ultrasound images from 130 patients with pelvic cancer were collected retrospectively. All data were split into a training set (80 patients), a validation set (20 patients), and a test set (30 patients). A total of 12 transabdominal ultrasound images for one patient were captured by automatically rotating the ultrasonic probe with an angle step of 15°. An incomplete 3D ultrasound volume was synthesized by arranging these 2D ultrasound images in 3D space according to the acquisition angles. With this as input, a weakly supervised learning‐based 3D bladder reconstruction neural network model was built to predict the complete 3D bladder. The key point is that we designed a novel loss function, including the supervised loss of bladder segmentation in the ultrasound images at known angles and the compactness loss of the 3D bladder. Bladder volume was calculated by counting the number of voxels belonging to the 3D bladder. The dice similarity coefficient (DSC) was used to evaluate the accuracy of bladder segmentation, and the relative standard deviation (RSD) was used to evaluate the calculation accuracy of bladder volume with that of computed tomography (CT) images as the gold standard.ResultsThe results showed that the mean DSC was up to 0.94 and the mean absolute RSD can be reduced to 6.3% when using 12 ultrasound images of one patient. Further, the mean DSC also was up to 0.90 and the mean absolute RSD can be reduced to 9.0% even if only two ultrasound images were used (i.e., the angle step is 90°). Compared with the commercial algorithm in bladder scanners, which has a mean absolute RSD of 13.6%, our proposed method showed a considerably huge improvement.ConclusionsThe proposed weakly supervised learning‐based 3D bladder reconstruction method can greatly improve the accuracy of bladder volume measurement. It has great potential to be used in bladder volume measurement devices in the future.

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Section: Costeffectivenessmentioning

confidence: 99%

Weakly supervised learning‐based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement

et al. 2023

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“…Continuous bladder volume monitoring is helpful to diagnose incontinence or urine retention and to reduce the need for indwelling urinary catheter 9 . For instance, post-operative urinary retention (POUR) is defined as the inability to urinate after a surgical procedure despite having a full bladder and affects up to 70% of surgery patients 10 .…”

Section: Introduction Wearable Ultrasound Devices For Continuous Moni...mentioning

confidence: 99%

Memory-efficient low-compute segmentation algorithms for edge ultrasound devices: continuous bladder monitoring

Song

Asiedu

Wang

et al. 2023

Preprint

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Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is intermittently used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the risk of catheter-associated urinary tract infection. Wearable ultrasound devices combined with machine-learning based bladder volume estimation algorithms reduce the burdens of nurses in hospital settings and improve outpatient care. However, existing algorithms are memory and computation intensive, thereby demanding the use of expensive GPUs. In this paper, we develop and validate a low-compute memory-efficient deep learning model for accurate bladder region segmentation and urine volume calculation. B-mode ultrasound bladder images of 360 patients were divided into training and validation sets; another 74 patients were used as the test dataset. Our 1-bit quantized models with 4-bits and 6-bits skip connections achieved an accuracy within 3.8% and 2.6%, respectively, of a full precision state-of-the-art neural network (NN) without any floating-point operations and with an 11.5× and 9.0× reduction in memory requirements to fit under 150 kB. The means and standard deviations of the volume estimation errors, relative to estimates from ground-truth clinician annotations, were 5.0±33 ml and 6.8±29 ml, respectively. This lightweight NN can be easily integrated on the wearable ultrasound device for automated and continuous monitoring of urine volume. Our approach can potentially be extended to other clinical applications such as monitoring blood pressure and fetal heart rate.

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“…Continuous bladder volume monitoring helps to diagnose incontinence or urine retention and reduces the need for indwelling urinary catheters 9 . For instance, up to of surgery patients experience post-operative urinary retention (POUR), the inability to urinate after a surgical procedure despite having a full bladder 10 .…”

Section: Introductionmentioning

confidence: 99%

Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices

Song,

Asiedu,

Wang

et al. 2023

Sci Rep

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Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the risk of catheter-associated urinary tract infection. Wearable ultrasound devices combined with machine-learning based bladder volume estimation algorithms reduce the burdens of nurses in hospital settings and improve outpatient care. However, existing algorithms are memory and computation intensive, thereby demanding the use of expensive GPUs. In this paper, we develop and validate a low-compute memory-efficient deep learning model for accurate bladder region segmentation and urine volume calculation. B-mode ultrasound bladder images of 360 patients were divided into training and validation sets; another 74 patients were used as the test dataset. Our 1-bit quantized models with 4-bits and 6-bits skip connections achieved an accuracy within $$3.8\%$$ 3.8 % and $$2.6\%$$ 2.6 % , respectively, of a full precision state-of-the-art neural network (NN) without any floating-point operations and with an $$11.5\times$$ 11.5 × and $$9.0\times$$ 9.0 × reduction in memory requirements to fit under 150 kB. The means and standard deviations of the volume estimation errors, relative to estimates from ground-truth clinician annotations, were $$5.0\pm 33$$ 5.0 ± 33 ml and $$6.8\pm 29$$ 6.8 ± 29 ml, respectively. This lightweight NN can be easily integrated on the wearable ultrasound device for automated and continuous monitoring of urine volume. Our approach can potentially be extended to other clinical applications, such as monitoring blood pressure and fetal heart rate.

show abstract

Novel Three-Dimensional Bladder Reconstruction Model from B-Mode Ultrasound Image to Improve the Accuracy of Bladder Volume Measurement

Cited by 8 publications

References 24 publications

Weakly supervised learning‐based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement

Weakly supervised learning‐based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement

Memory-efficient low-compute segmentation algorithms for edge ultrasound devices: continuous bladder monitoring

Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices

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