W-Net: Dense and diagnostic semantic segmentation of subcutaneous and breast tissue in ultrasound images by incorporating ultrasound RF waveform data

Gare, Gautam Rajendrakumar; Li, Jiayuan; Joshi, Rohan; Magar, Rishikesh; Vaze, Mrunal Prashant; Yousefpour, Michael; Rodriguez, Ricardo Luis; Galeotti, John

doi:10.1016/j.media.2021.102326

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…BI-RADS categories are defined by the American Cancer Society; 6: biopsy-proven malignancy, 5: highly suggestive of malignancy >95%, 4: suspicious for malignancy 2-94%, 3: probably benign, malignancy <2%, 2: benign, 1: negative, 0: incomplete. These findings outperform previous research (Jarosik et al 2020, Byra et al 2022, Gare et al 2022 on utilizing RF data as AI input for breast classification, in which AUC were between 0.77 and 0.92. Moreover, there are studies (Uniyal et al 2014, Taleghamar et al 2022 utilizing RF data and extracting features for machine learning inputs with a similar approach as this study.…”

Section: Discussioncontrasting

confidence: 46%

“…These findings outperform previous research (Jarosik et al 2020 , Byra et al 2022 , Gare et al 2022 ) on utilizing RF data as AI input for breast classification, in which AUC were between 0.77 and 0.92. Moreover, there are studies (Uniyal et al 2014 , Taleghamar et al 2022 ) utilizing RF data and extracting features for machine learning inputs with a similar approach as this study.…”

Section: Discussionsupporting

confidence: 45%

“…Byra et al ( 2022 ) utilized RF data as deep learning input and achieved higher area under the curve (AUC) than B-mode for breast lesion classification and segmentation. Similarly, Gare et al ( 2022 ) used RF data along with B-mode for CNN input for the diagnostic labeling of benign and malignant, resulting in higher AUC than only using B-mode. Those studies have demonstrated that utilizing RF data can improve breast lesion classification performance compared to B-mode image input.…”

Section: Introductionmentioning

confidence: 99%

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Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Baek

O’Connell

Parker

2022

Mach. Learn.: Sci. Technol.

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The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to further-more provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were seg-mented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature’s data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic (ROC) curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the prob-ability and Breast Imaging Reporting and Data System (BI-RADS) enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

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Section: Discussioncontrasting

confidence: 46%

Section: Discussionsupporting

confidence: 45%

Section: Introductionmentioning

confidence: 99%

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Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Baek

O’Connell

Parker

2022

Mach. Learn.: Sci. Technol.

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“…The application of the proposed model implied the viability of a dual intended automated system with segmentation and extraction of low-dimensional deep radiomics from ROI. The proposed model exhibited relatively considerable accuracy compared to the state-of-art models, i.e., ASS-GANs [ 54 ], and W-Net [ 55 ], yet the combination of concurrent low-dimensional radiomics increases the contribution of this model, which significantly reduces the training process and required data compared cascading multiple models. Similarly, our model exhibited considerable growth in model performance for classifying breast cancer patients from benign cases ( Figure 7 , and Table 2 ).…”

Section: Discussionmentioning

confidence: 99%

Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging

Vigil

Barry

Amini

et al. 2022

Cancers

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Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.

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“…As shown in Figure 4 , the proposed network architecture consists of two U-Net blocks. The method comprising two U-blocks has been proven to obtain better performance than one U-block in the image analysis area [ 22 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram

et al. 2022

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The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals.

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W-Net: Dense and diagnostic semantic segmentation of subcutaneous and breast tissue in ultrasound images by incorporating ultrasound RF waveform data

Cited by 20 publications

References 17 publications

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging

Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram

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