Ultrasound Viscoelastic Imaging of Breast Lesions: A Practical Hybrid Freehand Technique for Data Acquisition

2011 ◽  
Author(s):  
Andrew Di Battista ◽  
J. Alison Noble ◽  
Ruth English
Keyword(s):  
Tissue Characterization ◽  
Time Sequence ◽  
Model Fit ◽  
Frame Rate ◽  
Supplementary Information ◽  
Ultrasound Images ◽  
Clinical Patient ◽  
Parametric Data ◽  
Freehand Technique ◽  
Applied Forces

Ultrasound imaging of the breast is a standard method in breast cancer screening, along with mammography. The viscoelastic properties of soft tissue can provide supplementary information for radiologist to consider in their assessment of pathology and tissue characterization. Measuring these properties generally entails acquiring a time sequence of ultrasound images and calculating parametric data from it. As images are necessarily accumulated over time, acquisition is limited by the frame rate and memory capacity of the ultrasound machine, and practical considerations such as movement from the clinicians hands and patient breathing. This paper describes a technique for hybrid-freehand imaging of viscoelasticity (HYFIVE). It involves acquiring a time sequence of images making use of a simple purpose built canister enclosure for the ultrasound probe which allows for a stable and accurate manipulation of applied forces, without the need of motors, sensors or other sophisticated and costly parts. A sequence of ultrasound strain images was computed and a first order Kelvin-Voigt tissue model fit to the resulting strain vs. time curves to obtain parametric data related to tissue stiffness and viscosity. Experiments were conducted on both gelatin phantoms and clinical patient data.

scholarly journals Ultrasonic Measurement of Temperature Rise in Breast Cyst and in Neighbouring Tissues as a Method of Tissue Differentiation

2016 ◽  
Vol 41 (4) ◽  
pp. 791-798 ◽  
Author(s):  
Barbara Gambin ◽  
Michał Byra ◽  
Eleonora Kruglenko ◽  
Olga Doubrovina ◽  
Andrzej Nowicki
Keyword(s):  
Probability Density ◽  
Breast Tissue ◽  
Tissue Characterization ◽  
Heating Process ◽  
Ultrasound Images ◽  
Bioheat Equation ◽  
Acoustical Properties ◽  
Breast Cyst ◽  
Malignant Lesions ◽  
Backscattered Signals

Abstract Texture of ultrasound images contain information about the properties of examined tissues. The analysis of statistical properties of backscattered ultrasonic echoes has been recently successfully applied to differentiate healthy breast tissue from the benign and malignant lesions. We propose a novel procedure of tissue characterization based on acquiring backscattered echoes from the heated breast. We have proved that the temperature increase inside the breast modifies the intensity, spectrum of the backscattered signals and the probability density function of envelope samples. We discuss the differences in probability density functions in two types of tissue regions, e.g. cysts and the surrounding glandular tissue regions. Independently, Pennes bioheat equation in heterogeneous breast tissue was used to describe the heating process. We applied the finite element method to solve this equation. Results have been compared with the ultrasonic predictions of the temperature distribution. The results confirm the possibility of distinguishing the differences in thermal and acoustical properties of breast cyst and surrounding glandular tissues.

scholarly journals Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251899
Author(s):  
Samir M. Badawy ◽  
Abd El-Naser A. Mohamed ◽  
Alaa A. Hefnawy ◽  
Hassan E. Zidan ◽  
Mohammed T. GadAllah ◽  
...  
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Tissue Characterization ◽  
Semantic Segmentation ◽  
Batch Processing ◽  
Ultrasound Images ◽  
Processing Scheme ◽  
Processing Mode ◽  
Small Dataset

Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

scholarly journals Analysis of Cardiovascular Tissue Components for the Diagnosis of Coronary Vulnerable Plaque from Intravascular Ultrasound Images

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Ju Hwan Lee ◽  
Yoo Na Hwang ◽  
Ga Young Kim ◽  
Eun Seok Shin ◽  
Sung Min Kim
Keyword(s):  
Intravascular Ultrasound ◽  
Vulnerable Plaque ◽  
Tissue Characterization ◽  
Plaque Rupture ◽  
Fibrous Tissue ◽  
Elastic Membrane ◽  
Ultrasound Images ◽  
Tissue Properties ◽  
Cardiovascular Tissue ◽  
The Media

The purpose of this study was to characterize cardiovascular tissue components and analyze the different tissue properties for predicting coronary vulnerable plaque from intravascular ultrasound (IVUS) images. For this purpose, sequential IVUS image frames were obtained from human coronary arteries using 20 MHz catheters. The plaque regions between the intima and media-adventitial borders were manually segmented in all IVUS images. Tissue components of the plaque regions were classified into having fibrous tissue (FT), fibrofatty tissue (FFT), necrotic core (NC), or dense calcium (DC). The media area and lumen diameter were also estimated simultaneously. In addition, the external elastic membrane (EEM) was computed to predict the vulnerable plaque after the tissue characterization. The reliability of manual segmentation was validated in terms of inter- and intraobserver agreements. The quantitative results found that the FT and the media as well as the NC would be good indicators for predicting vulnerable plaques in IVUS images. In addition, the lumen was not suitable for early diagnosis of vulnerable plaque because of the low significance compared to the other vessel parameters. To predict vulnerable plaque rupture, future study should have additional experiments using various tissue components, such as the EEM, FT, NC, and media.

scholarly journals Novel Multimodal, Multiscale Imaging System with Augmented Reality

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 441
Author(s):  
Christopher Mela ◽  
Francis Papay ◽  
Yang Liu
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Fluorescence Imaging ◽  
Imaging System ◽  
Frame Rate ◽  
Prototype System ◽  
Ultrasound Images ◽  
Fiducial Markers ◽  
Registration Accuracy ◽  
Multiscale Imaging

A novel multimodal, multiscale imaging system with augmented reality capability were developed and characterized. The system offers 3D color reflectance imaging, 3D fluorescence imaging, and augmented reality in real time. Multiscale fluorescence imaging was enabled by developing and integrating an in vivo fiber-optic microscope. Real-time ultrasound-fluorescence multimodal imaging used optically tracked fiducial markers for registration. Tomographical data are also incorporated using optically tracked fiducial markers for registration. Furthermore, we characterized system performance and registration accuracy in a benchtop setting. The multiscale fluorescence imaging facilitated assessing the functional status of tissues, extending the minimal resolution of fluorescence imaging to ~17.5 µm. The system achieved a mean of Target Registration error of less than 2 mm for registering fluorescence images to ultrasound images and MRI-based 3D model, which is within clinically acceptable range. The low latency and high frame rate of the prototype system has shown the promise of applying the reported techniques in clinically relevant settings in the future.

scholarly journals Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

2020 ◽  
Author(s):  
Liang Dong ◽  
Wei Lu ◽  
Jun Jiang ◽  
Ya Zhao ◽  
Xiangfen Song ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Intravascular Ultrasound ◽  
Tissue Characterization ◽  
Data Augmentation ◽  
Automatic Segmentation ◽  
Model Performance ◽  
Single Frequency ◽  
Elastic Membrane ◽  
Ultrasound Images ◽  
External Elastic Membrane

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e. cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (mIoU) of 0.937 and 0.804 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician.Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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