3D automated breast ultrasound imaging: equipment and image reconstruction

Luccas, Vivian A. S.; Azimbagirad, Mehran; Cabrelli, Luciana C.; Grillo, Felipe Fardin; Pavan, Theo Z.; Murta, Leonardo; Elias, Jorge; Carneiro, Adriana Munhoz

doi:10.1007/s42600-021-00159-y

Cited by 6 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there are other ways for noise reduction or signal recovery such as curve fitting, attenuation filter for noisy signal (Luccas et al, 2021), several researchers have sought to overcome these challenges through the use of machine learning (ML) (AlAbboudi et al, 2020, Gayathri et al, 2018, Gemein et al, 2020, Ieracitano et al, 2021, Isa et al, 2019, Mardini et al, 2020, Rashid et al, 2018. Among the various areas within machine learning, deep learning (DL) algorithms have stood out in recent years.…”

Section: Context and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

ASTERI: image-based representation of EEG signals for motor imagery classification

2022

View full text Add to dashboard Cite

Electroencephalography (EEG) signals are valuable in the monitoring and investigation of neurological diseases and in the control of brain-machine interfaces (BCI). However, these signals are noisy and are non-linear and non-stationary in nature. Signal analysis is an expensive task and can lead to misdiagnosis. Deep learning can be used to overcome these challenges. The most used deep architectures are based on convolutional neural networks (CNNs). Representing EEG signals as images can be useful to use deep architectures based on CNNs in solutions based on intelligent EEG-based systems. Methods: In this work, we propose the ASTERI method, for representing EEG signals in two dimensions using the backprojection reconstruction method. To validate the proposal, experiments were performed with motor imagery EEG signals from the BCI Competition IV 2b motor imagery database. To extract attributes from the EEG signal windows, we used the ASTERI representation and investigated the pre-trained networks VGG16 and LeNet. To classify the final classification of imagined movements to the right and to the left, we used Random Forests. Results: For the original database, the validation with 100 trees achieved an average accuracy and kappa index of 88.97% and 0.78, respectively. In the case of augmented databases with synthetic instances, the average accuracies were 89.10% and 89.13% for increases of 50% and 150% for each class, respectively. Conclusion: The experimental results showed that the ASTERI method, together with hybrid deep architectures based on CNN and Random Forests, can generate competitive results in solving motor imagery problems. The representation of signals as images can also con tribute to the exploration of new solutions of applied neuroscience problems based on deep architectures.

show abstract

Section: Context and Motivationmentioning

confidence: 99%

“…In this way, DL methods implicitly extract attributes. Among deep architectures, convolutional neural networks (CNN) are quite popular, being able to capture an input image, assign learnable weights to various aspects and objects of the image, and thus classify the image (Mao et al, 2020).…”

Section: Context and Motivationmentioning

confidence: 99%

ASTERI: image-based representation of EEG signals for motor imagery classification

2022

View full text Add to dashboard Cite

show abstract

“…Alternative prone ABUS systems have been developed, including but not limited to, the commercially available SonixEmbrace™ (Ultrasonix Medical Corporation, British Columbia, Canada), SVARATM™ (TechniScan Inc., Utah, USA), and SOFIA™ (Hitachi Ltd, Tokyo, Japan) systems. [36][37][38][39][40][41] Recent studies show that prone-based systems are promising for screening women with dense breasts with a diagnostic performance comparable to supine ABUS systems. 38 Furthermore, methods beyond conventional US, such as US-computed tomography (USCT), have been previously developed and shown useful for whole-breast 3D imaging.…”

Section: Introductionmentioning

confidence: 99%

Spatially tracked whole‐breast three‐dimensional ultrasound system toward point‐of‐care breast cancer screening in high‐risk women with dense breasts

et al. 2022

View full text Add to dashboard Cite

Background Mammographic screening has reduced mortality in women through the early detection of breast cancer. However, the sensitivity for breast cancer detection is significantly reduced in women with dense breasts, in addition to being an independent risk factor. Ultrasound (US) has been proven effective in detecting small, early‐stage, and invasive cancers in women with dense breasts. Purpose To develop an alternative, versatile, and cost‐effective spatially tracked three‐dimensional (3D) US system for whole‐breast imaging. This paper describes the design, development, and validation of the spatially tracked 3DUS system, including its components for spatial tracking, multi‐image registration and fusion, feasibility for whole‐breast 3DUS imaging and multi‐planar visualization in tissue‐mimicking phantoms, and a proof‐of‐concept healthy volunteer study. Methods The spatially tracked 3DUS system contains (a) a six‐axis manipulator and counterbalanced stabilizer, (b) an in‐house quick‐release 3DUS scanner, adaptable to any commercially available US system, and removable, allowing for handheld 3DUS acquisition and two‐dimensional US imaging, and (c) custom software for 3D tracking, 3DUS reconstruction, visualization, and spatial‐based multi‐image registration and fusion of 3DUS images for whole‐breast imaging. Spatial tracking of the 3D position and orientation of the system and its joints (J1–6) were evaluated in a clinically accessible workspace for bedside point‐of‐care (POC) imaging. Multi‐image registration and fusion of acquired 3DUS images were assessed with a quadrants‐based protocol in tissue‐mimicking phantoms and the target registration error (TRE) was quantified. Whole‐breast 3DUS imaging and multi‐planar visualization were evaluated with a tissue‐mimicking breast phantom. Feasibility for spatially tracked whole‐breast 3DUS imaging was assessed in a proof‐of‐concept healthy male and female volunteer study. Results Mean tracking errors were 0.87 ± 0.52, 0.70 ± 0.46, 0.53 ± 0.48, 0.34 ± 0.32, 0.43 ± 0.28, and 0.78 ± 0.54 mm for joints J1–6, respectively. Lookup table (LUT) corrections minimized the error in joints J1, J2, and J5. Compound motions exercising all joints simultaneously resulted in a mean tracking error of 1.08 ± 0.88 mm (N = 20) within the overall workspace for bedside 3DUS imaging. Multi‐image registration and fusion of two acquired 3DUS images resulted in a mean TRE of 1.28 ± 0.10 mm. Whole‐breast 3DUS imaging and multi‐planar visualization in axial, sagittal, and coronal views were demonstrated with the tissue‐mimicking breast phantom. The feasibility of the whole‐breast 3DUS approach was demonstrated in healthy male and female volunteers. In the male volunteer, the high‐resolution whole‐breast 3DUS acquisition protocol was optimized without the added complexities of curvature and tissue deformations. With small post‐acquisition corrections for motion, whole‐breast 3DUS imaging was performed on the healthy female volunteer showing relevant anatomical structures and details. Conc...

show abstract

ASTERI: Image-based representation of EEG signals for motor imagery classification

Gomes

Rodrigues

Santos

2022

Preprint

View full text Add to dashboard Cite

Purpose Electroencephalography (EEG) signals are valuable in the monitoring and investigation of neurological diseases and in the control of brain-machine interfaces (BCI). However, these signals are noisy and are non-linear and non-stationary in nature. Signal analysis is an expensive task and can lead to misdiagnosis. Deep learning can be used to overcome these challenges. The most used deep architectures are based on convolutional neural networks (CNNs). Representing EEG signals as images can be useful to use deep architectures based on CNNs in solutions based on intelligent EEG-based systems. Methods In this work, we propose the ASTERI method, for representing EEG signals in two dimensions using the backprojection reconstruction method. To validate the proposal, experiments were performed with motor imagery EEG signals from the BCI Competition IV 2b motor imagery database. To extract attributes from the EEG signal windows, we used the ASTERI representation and investigated the pre-trained networks VGG16 and LeNet. To classify the final classification of imagined movements to the right and to the left, we used Random Forests. Results For the original database, the validation with 100 trees achieved an average accuracy and kappa index of 88.97% and 0.78, respectively. In the case of augmented databases with synthetic instances, the average accuracies were 89.10% and 89.13% for increases of 50% and 150% for each class, respectively. Conclusion The experimental results showed that the ASTERI method, together with hybrid deep architectures based on CNN and Random Forests, can generate competitive results in solving motor imagery problems. The representation of signals as images can also con tribute to the exploration of new solutions of applied neuroscience problems based on deep architectures.

show abstract

3D automated breast ultrasound imaging: equipment and image reconstruction

Cited by 6 publications

References 38 publications

ASTERI: image-based representation of EEG signals for motor imagery classification

ASTERI: image-based representation of EEG signals for motor imagery classification

Spatially tracked whole‐breast three‐dimensional ultrasound system toward point‐of‐care breast cancer screening in high‐risk women with dense breasts

ASTERI: Image-based representation of EEG signals for motor imagery classification

Contact Info

Product

Resources

About