Dilated Residual Learning With Skip Connections for Real-Time Denoising of Laser Speckle Imaging of Blood Flow in a Log-Transformed Domain

Cheng, Weimin; Lu, Jinling; Zhu, Xuan; Hong, Jiachi; Xiao-hu, Liu; Li, Miaowen; Li, Pengcheng

doi:10.1109/tmi.2019.2953626

Cited by 18 publications

(5 citation statements)

References 38 publications

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“…There is a pressing need in clinical medicine for an accurate diagnostic approach to detect the lesion region of a liver tumour. Artificial intelligence has improved picture categorization for deep learning during the last few years [14]. In the medical industry, Artificial Intelligence (AI) can have a significant impact on disease detection and therapy [15].…”

Section: Figure 1 Stages Of Liver Tumormentioning

confidence: 99%

CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

Gavini¹,

Lakshmi²

2022

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Image denoising is an important concept in image processing for improving the image quality. It is difficult to remove noise from images because of the various causes of noise. Imaging noise is made up of many different types of noise, including Gaussian, impulse, salt, pepper, and speckle noise. Increasing emphasis has been paid to Convolution Neural Networks (CNNs) in image denoising. Image denoising has been researched using a variety of CNN approaches. For the evaluation of these methods, various datasets were utilized. Liver Tumor is the leading cause of cancer-related death worldwide. By using Computed Tomography (CT) to detect liver tumor early, millions of patients could be spared from death each year. Denoising a picture means cleaning up an image that has been corrupted by unwanted noise. Due to the fact that noise, edge, and texture are all high frequency components, denoising can be tricky, and the resulting images may be missing some finer features. Applications where recovering the original image content is vital for good performance benefit greatly from image denoising, including image reconstruction, activity recognition, image restoration, segmentation techniques, and image classification. Tumors of this type are difficult to detect and are almost always discovered at an advanced stage, posing a serious threat to the patient's life. As a result, finding a tumour at an early stage is critical. Tumors can be detected non-invasively using medical image processing. There is a pressing need for software that can automatically read, detect, and evaluate CT scans by removing noise from the images. As a result, any system must deal with a bottleneck in liver segmentation and extraction from CT scans. To segment and classify liver CT images after denoising images, a deep CNN technique is proposed in this research. An Image Quality Enhancement model with Image Denoising and Edge based Segmentation (IQE-ID-EbS) is proposed in this research that effectively reduces noise levels in the image and then performs edge based segmentation for feature extraction from the CT images. The proposed model is compared with the traditional models and the results represent that the proposed model performance is better.

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Section: Figure 1 Stages Of Liver Tumormentioning

confidence: 99%

CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

Gavini¹,

Lakshmi²

2022

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“…Recent work on DL in MECI includes the use of convolutional neural networks (CNN) to classify and segment blood vessels, recurrent neural networks (RNN) to analyze dynamic data and provide more accurate measurements of blood flow, and generative adversarial networks (GAN) to reconstruct dynamic MECI images [ 14 ]. Cheng et al have presented a dilated residual learning (a feed-forward denoising CNN) along skip connections for real-time denoising LSCI of blood flow in the log-transformed domain, which minimizes the inference time and maximizes denoising performance [ 15 ]. Fredriksson et al suggested a method for calculating a high precision perfusion prediction from LDF utilizing contrasts of seven exposure times between one and 64 ms, with a correlation analysis of 1 for noise-free data, 0.993 for moderate noise levels, and 0.995 for in vivo occlusion-release data [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Blood Flow Prediction in Multi-Exposure Speckle Contrast Imaging Using Conditional Generative Adversarial Network

Jain¹,

Gupta²

2023

Cureus

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Blood perfusion is an important physiological parameter that can be quantitatively assessed using various imaging techniques. Blood flow prediction in laser speckle contrast imaging is important for medical diagnosis, drug development, tissue engineering, biomedical research, and continuous monitoring. Deep learning is a new and promising approach for predicting blood flow whenever the condition varies, but it comes with a high learning cost for real-world scenarios with a variable flow value derived from multiexposure laser speckle contrast imaging (MECI) data. A generative adversarial network (GAN) is presented in this research for the reliable prediction of blood flows in diverse scenarios in MECI. MethodWe suggested a time-efficient approach using a low frame rate camera that can be used to predict blood flow in MECI data by using conditional GAN architecture. Our approach is implemented by extending our work to the entire flow as well as the specific region of interest (ROI) in the flow. ResultsResults show that conditional GAN exhibits improved generalization ability to predict blood flow in MECI when compared to classifications-based deep learning approaches with an accuracy of 98.5% with a relative mean error of 1.57% for the whole field and 7.53% for a specific ROI. ConclusionThe conditional GAN is very effective in predicting blood flows in MECI, entirely or within ROI, compared with other deep learning approaches.

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“…Even though blood vessel segmentation in LSCI is an attractive topic, due to the low signal-noise ratio, relatively small and numerous blood vessels, there are still several obstacles that have limited the performance of vessel segmentation in LSCI images (Cheng et al 2020). Meanwhile, very limited attempts have been made in the area of automated LSCI vessel segmentation over the last decades.…”

Section: Introductionmentioning

confidence: 99%

Robust vessel segmentation in laser speckle contrast images based on semi-weakly supervised learning

Yang,

Chang,

Yuan

et al. 2023

Phys. Med. Biol.

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Objective. The goal of this study is to develop a robust semi-weakly supervised learning strategy for vessel segmentation in laser speckle contrast imaging (LSCI), addressing the challenges associated with the low signal-to-noise ratio, small vessel size, and irregular vascular aberration in diseased regions, while improving the performance and robustness of the segmentation method. Approach. For the training dataset, the healthy vascular images denoted as normal-vessel samples were manually labeled, while the diseased LSCI images involving tumor or embolism were denoted as abnormal-vessel samples and annotated as pseudo labels by the traditional semantic segmentation methods. In the training phase, the pseudo labels were constantly updated to improve the segmentation accuracy based on DeepLabv3+. Objective evaluation was conducted on the normal-vessel test set, while subjective evaluation was performed on the abnormal-vessel test set. Main results. The proposed method achieved an IOU of 0.8671, a Dice of 0.9288, and a mean relative percentage difference (mRPD) with supervised learning of 0.5% in the objective evaluation. In the subjective evaluation, our method significantly outperformed other methods in main vessel segmentation, tiny vessel segmentation, and blood vessel connection. Additionally, our method exhibited robustness when abnormal-vessel style noise was added to normal-vessel samples using a style translation network. Significance. The proposed semi-weakly supervised learning strategy demonstrates high efficiency and excellent robustness for vascular segmentation in LSCI, providing a potential tool for assessing the morphological and structural features of vessels in clinical applications.

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Dilated Residual Learning With Skip Connections for Real-Time Denoising of Laser Speckle Imaging of Blood Flow in a Log-Transformed Domain

Cited by 18 publications

References 38 publications

CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

Blood Flow Prediction in Multi-Exposure Speckle Contrast Imaging Using Conditional Generative Adversarial Network

Robust vessel segmentation in laser speckle contrast images based on semi-weakly supervised learning

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