Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 10 de 10
Filtrar
Mais filtros








Base de dados
Intervalo de ano de publicação
1.
Artigo em Inglês | MEDLINE | ID: mdl-35452387

RESUMO

Light-weight segmentation models are becoming more popular for fast diagnosis on small and low cost medical imaging devices. This study focuses on the segmentation of the left ventricle in cardiac ultrasound images. A new light-weight model (LVNet) is proposed for segmentation which gives the benefits of requiring fewer parameters but with improved segmentation performance in terms of Dice score. The proposed model is compared with state-of-the-art methods such as UNet, MiniNetV2, and FCdDN. The model proposed comes with a post-processing pipeline that further enhances the segmentation results. Generally, the training is done directly using the segmentation mask as the output and the ultrasound image as the input of the model. A new strategy for segmentation is also introduced in addition to the direct training method used. Compared to the UNet model, an improvement in Dice score performance as high as 5% for segmentation with papillary muscles was found, while showcasing an improvement of 18.5% when the papillary muscles are excluded. The model proposed requires only 5% of the memory required by a UNet model. LVNet achieves a better trade-off between the number of parameters and its segmentation performance as compared to other conventional models. The developed codes are available at https://github.com/navchetan-awasthi/LeftVentricleSegmentation.

2.
Sensors (Basel) ; 21(23)2021 Nov 28.
Artigo em Inglês | MEDLINE | ID: mdl-34883951

RESUMO

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.


Assuntos
Cardiologia , Sistema Cardiovascular , Técnicas Fotoacústicas , Diagnóstico por Imagem , Ultrassonografia
3.
Biomed Opt Express ; 12(3): 1320-1338, 2021 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-33796356

RESUMO

The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data. The method was shown to be superior as compared to total variation regularization, basis pursuit deconvolution and Lanczos Tikhonov based regularization and provided improved performance in case of noisy data. The numerical and experimental cases show that the improvement can be as high as 8.1 dB in signal to noise ratio of the reconstructed image and 67.98% in root mean square error in comparison to the state of the art methods.

4.
Artigo em Inglês | MEDLINE | ID: mdl-33755565

RESUMO

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with other lightweight neural network models along with state-of-the-art heavy model. It was shown that the proposed network can achieve the highest accuracy of 83.2% and requires a training time of only 24 min. The proposed Mini-COVIDNet has 4.39 times less number of parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB, making the point-of-care detection of COVID-19 using lung US imaging plausible on a mobile platform. Deployment of these lightweight networks on embedded platforms shows that the proposed Mini-COVIDNet is highly versatile and provides optimal performance in terms of being accurate as well as having latency in the same order as other lightweight networks. The developed lightweight models are available at https://github.com/navchetan-awasthi/Mini-COVIDNet.


Assuntos
COVID-19/diagnóstico por imagem , Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Sistemas Automatizados de Assistência Junto ao Leito , Ultrassonografia/métodos , Humanos , SARS-CoV-2
5.
Telemed J E Health ; 27(12): 1433-1439, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-33729001

RESUMO

Background: We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Methods: Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine (n = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. Results: The median relative estimation errors were 17%, -4%, 35%, -3%, -9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other (p < 0.05). Conclusions: We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.


Assuntos
Eletrocardiografia , Smartphone , Animais , Suínos
6.
J Biophotonics ; 13(11): e202000123, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-33245636

RESUMO

Low-cost automated histopathology microscopy systems usually suffer from optical imperfections, producing images that are slightly Out of Focus (OoF). In this work, a guided filter (GF) based image preprocessing is proposed for compensating focal errors and its efficacy is demonstrated on images of healthy and malaria infected red blood cells (h-RBCs and i-RBCs), and PAP smears. Since contrast enhancement has been widely used as an image preprocessing step for the analysis of histopathology images, a systematic comparison is made with six such prominently used methods, namely Contrast Limited Adaptive Histogram Equalization (CLAHE), RIQMC-based optimal histogram matching (ROHIM), modified L0, Morphological Varying(MV)-Bitonic filter, unsharp mask filter and joint bilateral filter. The images enhanced using GF approach lead to better segmentation accuracy (upto 50% improvement over native images) and visual quality compared to other approaches, without any change in the color tones. Thus, the proposed GF approach is a viable solution for rectifying the OoF microscopy images without the loss of the valuable diagnostic information presented by the color tone.


Assuntos
Algoritmos , Aumento da Imagem , Feminino , Humanos
7.
Artigo em Inglês | MEDLINE | ID: mdl-32142429

RESUMO

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Técnicas Fotoacústicas/métodos , Tomografia/métodos , Imagens de Fantasmas , Transdutores
8.
Biomed Opt Express ; 10(5): 2227-2243, 2019 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-31149371

RESUMO

The methods available for solving the inverse problem of photoacoustic tomography promote only one feature-either being smooth or sharp-in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data for training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and in-vivo studies. The improvement obtained in the reconstructed images was as high as 95.49% in root mean square error and 7.77 dB in signal to noise ratio (SNR) in comparison to the guided filter approach. Also, it was demonstrated that the proposed deep fuse approach, trained on only blood vessel type images at measurement data SNR being 40 dB, was able to provide a generalization that can work across various noise levels in the measurement data, experimental set-ups as well as imaging objects.

9.
J Biomed Opt ; 23(9): 1-22, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29943527

RESUMO

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Técnicas Fotoacústicas/métodos , Tomografia/métodos , Algoritmos , Animais , Encéfalo/diagnóstico por imagem , Feminino , Imagens de Fantasmas , Ratos , Processamento de Sinais Assistido por Computador , Razão Sinal-Ruído
10.
J Biomed Opt ; 23(7): 1-11, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29405050

RESUMO

As limited data photoacoustic tomographic image reconstruction problem is known to be ill-posed, the iterative reconstruction methods were proven to be effective in terms of providing good quality initial pressure distribution. Often, these iterative methods require a large number of iterations to converge to a solution, in turn making the image reconstruction procedure computationally inefficient. In this work, two variants of vector polynomial extrapolation techniques were deployed to accelerate two standard iterative photoacoustic image reconstruction algorithms, including regularized steepest descent and total variation regularization methods. It is shown using numerical and experimental phantom cases that these extrapolation methods that are proposed in this work can provide significant acceleration (as high as 4.7 times) along with added advantage of improving reconstructed image quality.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Técnicas Fotoacústicas/métodos , Algoritmos , Modelos Biológicos , Imagens de Fantasmas , Técnicas Fotoacústicas/instrumentação
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA