Diagnosis of Pregnancy Complications Using Blind Ultrasound Sweeps Performed by Individuals Without Prior Formal Ultrasound Training

Toscano, Marika; Marini, Thomas J.; Lennon, Clare; Erlick, Mariah; Silva, Hannah; Crofton, Kathryn; Serratelli, William; Rana, Neel; Dozier, Ann; Castañeda, Benjamín; Baran, Timothy M.; Drennan, Kathryn

doi:10.1097/aog.0000000000005139

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…VSI is an imaging technique in which an individual without prior ultrasound training performs blind sweeps of the ultrasound probe over a target region such as the breast. VSI has already been clinically tested for breast, lung, thyroid, right upper quadrant, and obstetrics indications [6,9,[73][74][75][76][77][78][79]. Individuals have been shown to be able to perform VSI after only a few hours of training [80,81].…”

Section: Discussionmentioning

confidence: 99%

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Khaledyan,

Marini,

M. Baran

et al. 2023

PLoS ONE

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Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar’s statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.

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Section: Discussionmentioning

confidence: 99%

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Khaledyan,

Marini,

M. Baran

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet

Khaledyan

Marini

O’Connell

et al. 2023

Preprint

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Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it a popular choice among researchers in the medical image segmentation field. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the dice coefficient, specificity, sensitivity, and F1 score obtained values of 0.9283, 0.9936, 0.9426, and 0.9412, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperforms the earlier designed models and points towards improved breast lesion segmentation algorithms.

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Diagnosis of Pregnancy Complications Using Blind Ultrasound Sweeps Performed by Individuals Without Prior Formal Ultrasound Training

Cited by 2 publications

References 37 publications

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet

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