Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

Vyas, Nina; Mahmud, Mehdi; Wang, Qianxi; Walmsley, A. D.

doi:10.3791/61509

Cited by 2 publications

(2 citation statements)

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“…In three different databases, the cell detection results of pathological images based on the optimized CNN algorithm in this study were compared with those of iterative radial voting (IRV) [ 23 ] and maximum stable extremum region (MSER) [ 24 ] (Figures 7 – 9 ). In three different databases, the ROC AUC of the optimized CNN algorithm was the maximum, which was significantly greater than the other two algorithms.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Technology in Pathological Image Analysis of Breast Tissue

Liu

Wang

et al. 2021

Journal of Healthcare Engineering

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To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms ( P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant ( P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.

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Section: Resultsmentioning

confidence: 99%

Deep Learning Technology in Pathological Image Analysis of Breast Tissue

Liu

Wang

et al. 2021

Journal of Healthcare Engineering

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“…Microbubbles or small spherical-shaped gas bubbles consist of phospholipids or environmentally friendly polymers are very small in size to approximately the size of red blood cells and are used in several forms in the biomedical field, including diagnostic loads, as drug carriers, and as a gene transporter in conjunction with the US [ 42 – 46 ]. Microbubbles have many fruitful uses in screening/diagnostic equipment [ 47 – 49 ], ophthalmology [ 50 , 51 ], dentistry [ 52 , 53 ], surgery [ 54 ], pharmacy [ 55 ], cardiovascular disease [ 56 ], inner ear drug delivery [ 57 ], brain delivery [ 58 ], renal diseases [ 59 ], cancer [ 60 , 61 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

The promising shadow of microbubble over medical sciences: from fighting wide scope of prevalence disease to cancer eradication

et al. 2021

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Microbubbles are typically 0.5–10 μm in size. Their size tends to make it easier for medication delivery mechanisms to navigate the body by allowing them to be swallowed more easily. The gas included in the microbubble is surrounded by a membrane that may consist of biocompatible biopolymers, polymers, surfactants, proteins, lipids, or a combination thereof. One of the most effective implementation techniques for tiny bubbles is to apply them as a drug carrier that has the potential to activate ultrasound (US); this allows the drug to be released by US. Microbubbles are often designed to preserve and secure medicines or substances before they have reached a certain area of concern and, finally, US is used to disintegrate microbubbles, triggering site-specific leakage/release of biologically active drugs. They have excellent therapeutic potential in a wide range of common diseases. In this article, we discussed microbubbles and their advantageous medicinal uses in the treatment of certain prevalent disorders, including Parkinson's disease, Alzheimer's disease, cardiovascular disease, diabetic condition, renal defects, and finally, their use in the treatment of various forms of cancer as well as their incorporation with nanoparticles. Using microbubble technology as a novel carrier, the ability to prevent and eradicate prevalent diseases has strengthened the promise of effective care to improve patient well-being and life expectancy.

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Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

Abstract: Walmsley, A.D. Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis.

Cited by 2 publications

References 31 publications

Deep Learning Technology in Pathological Image Analysis of Breast Tissue

Deep Learning Technology in Pathological Image Analysis of Breast Tissue

The promising shadow of microbubble over medical sciences: from fighting wide scope of prevalence disease to cancer eradication

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