scholarly journals Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lingxia Zhu ◽  
Zhiping Xu ◽  
Ting Fang
Keyword(s):  
Deep Learning ◽  
Critically Ill ◽  
Classification Accuracy ◽  
Ultrasound Image ◽  
Video Data ◽  
Ultrasound Images ◽  
Cardiac Ultrasound ◽  
Two Dimensional Image ◽  
Number Of Patients ◽  
New Treatments

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.

scholarly journals Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

2021 ◽  
pp. 29-42
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Ultrasound Image ◽  
Smart Devices ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Field ◽  
Modern Methods

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

scholarly journals Deep transfer learning for aortic root dilation identification in 3D ultrasound images

2018 ◽  
Vol 4 (1) ◽  
pp. 71-74 ◽  
Author(s):  
Jannis Hagenah ◽  
Mattias Heinrich ◽  
Floris Ernst
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Aortic Root ◽  
Classification Accuracy ◽  
3D Ultrasound ◽  
Small Data ◽  
Data Sets ◽  
Ultrasound Images ◽  
Small Data Sets ◽  
Learned Features

AbstractPre-operative planning of valve-sparing aortic root reconstruction relies on the automatic discrimination of healthy and pathologically dilated aortic roots. The basis of this classification are features extracted from 3D ultrasound images. In previously published approaches, handcrafted features showed a limited classification accuracy. However, feature learning is insufficient due to the small data sets available for this specific problem. In this work, we propose transfer learning to use deep learning on these small data sets. For this purpose, we used the convolutional layers of the pretrained deep neural network VGG16 as a feature extractor. To simplify the problem, we only took two prominent horizontal slices throgh the aortic root, the coaptation plane and the commissure plane, into account by stitching the features of both images together and training a Random Forest classifier on the resulting feature vectors. We evaluated this method on a data set of 48 images (24 healthy, 24 dilated) using 10-fold cross validation. Using the deep learned features we could reach a classification accuracy of 84 %, which clearly outperformed the handcrafted features (71 % accuracy). Even though the VGG16 network was trained on RGB photos and for different classification tasks, the learned features are still relevant for ultrasound image analysis of aortic root pathology identification. Hence, transfer learning makes deep learning possible even on very small ultrasound data sets.

scholarly journals Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhemin Zhuang ◽  
Zengbiao Yang ◽  
Shuxin Zhuang ◽  
Alex Noel Joseph Raj ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Breast Tumor ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Tumor Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Deep Learning Features ◽  
Characteristic Features

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

scholarly journals Deep Learning-Based Three-dimensional Transvaginal Ultrasound in Diagnosis of Intrauterine Adhesion

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Dan Liu ◽  
Xiaofeng Qing ◽  
Lanlan Yu ◽  
Huizhen Xiang
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Transvaginal Ultrasound ◽  
Three Dimensional ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Diagnostic Efficiency ◽  
Research Subjects ◽  
Partial Differential ◽  
Intrauterine Adhesion

This study was aimed to enhance and detect the characteristics of three-dimensional transvaginal ultrasound images based on the partial differential algorithm and HSegNet algorithm under deep learning. Thereby, the effect of quantitative parameter values of optimized three-dimensional ultrasound image was analyzed on the diagnosis and evaluation of intrauterine adhesions. Specifically, 75 patients with suspected intrauterine adhesion in hospital who underwent the hysteroscopic diagnosis were selected as the research subjects. The three-dimensional transvaginal ultrasound image was enhanced and optimized by the partial differential equation algorithm and processed by the deep learning algorithm. Subsequently, three-dimensional transvaginal ultrasound examinations were performed on the study subjects that met the standards. The March classification method was used to classify the patients with intrauterine adhesion. Finally, the results by the three-dimensional transvaginal ultrasound were compared with the diagnosis results in hysteroscope surgery. The results showed that the HSegNet algorithm model realized the automatic labeling of intrauterine adhesion in the transvaginal ultrasound image and the final accuracy coefficient was 97.3%. It suggested that the three-dimensional transvaginal ultrasound diagnosis based on deep learning was efficient and accurate. The accuracy of the three-dimensional transvaginal ultrasound was 97.14%, the sensitivity was 96.6%, and the specificity was 72%. In conclusion, the three-dimensional transvaginal examination can effectively improve the diagnostic efficiency of intrauterine adhesion, providing theoretical support for the subsequent diagnosis and grading of intrauterine adhesion.

scholarly journals DEEP LEARNING TO PREDICT DEGREE OF INTERSTITIAL FIBROSIS AND TUBULAR ATROPHY FROM KIDNEY ULTRASOUND IMAGES - AN ARTIFICIAL INTELLIGENCE APPROACH

2020 ◽  
Author(s):  
Ambarish M Athavale ◽  
Peter D Hart ◽  
Matthew Itteera ◽  
Tushar Patel ◽  
David J Cymbaluk ◽  
...  
Keyword(s):  
Deep Learning ◽  
Kidney Biopsy ◽  
Interstitial Fibrosis ◽  
Strong Predictor ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Tubular Atrophy ◽  
Invasive Test ◽  
Learning Classifier ◽  
Validation Set

Background: Interstitial fibrosis and tubular atrophy (IFTA) is a strong predictor of decline in kidney function. Non-invasive test to assess IFTA is not available. Methods: We trained, validated and tested a deep learning (DL) system to classify IFTA grade from 6,135 ultrasound images obtained from 352 patients who underwent kidney biopsy. Of 6,135 ultrasound images, 5,523 were used for training (n = 5,122) and validation (n = 401) and 612 to test the accuracy of the DL system. IFTA grade scored by nephropathologist on trichrome stained kidney biopsy slide was used as reference standard. Results: There were 159 patients (2,701 ultrasound images), 74 patients (1,239 ultrasound images), 41 patients (701 ultrasound images) and 78 patients (1,494 ultrasound images) with IFTA grades 1, 2, 3 and 4, respectively. The deep-learning classification system used masked images based on a 91% accurate kidney segmentation routine. The performance matrices for the deep learning classifier algorithm in the validation set showed excellent precision (90%), recall (76%), accuracy (84%) and F1-score (80%). In the independent test set also, performance matrices showed excellent precision (90%), recall (80%), accuracy (87%) and F1-score of (84%). Accuracy was highest for IFTA grade 1 (98%) and IFTA grade 4 (82%). Conclusion: A DL system can accurately predict IFTA from kidney ultrasound image.

scholarly journals Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Bian ◽  
Xiyu Zhang ◽  
Ruihong Liu ◽  
Huijie Li ◽  
Qingqing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Ultrasound Image ◽  
Color Doppler Ultrasound ◽  
Control Group ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Before And After ◽  
Experimental Group

The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant ( P < 0.05 ); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance ( P < 0.05 ). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.

scholarly journals Wavelet Transform-Based Ultrasound Image Enhancement Algorithm for Guided Gynecological Laparoscopy Imaging of Local Anesthetics in Perioperative Gynecological Laparoscopy

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bingbing Sheng ◽  
Qiaoqin Yan ◽  
Xianda Zhao ◽  
Wujian Yang
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Local Anesthetics ◽  
Ultrasound Image ◽  
Postoperative Nausea And Vomiting ◽  
Transversus Abdominis ◽  
Ultrasound Images ◽  
Tap Block ◽  
Gynecological Laparoscopy ◽  
Number Of Patients

This paper aimed to study the application of local anesthetics combined with transversus abdominis plane (TAP) block in gynecological laparoscopy (GLS) surgery during perioperative period under the guidance of ultrasound image enhanced by the wavelet transform image enhancement (WTIE) algorithm. 56 patients who underwent GLS surgery in hospital were selected and classified as the infiltrating group and block group. The puncture needle was guided by ultrasound images under WTIE algorithm, and 0.375% ropivacaine was adopted to block TAP. The results showed that the dosage of propofol in the infiltrating group (313.23 ± 19.67 mg) was remarkably inferior to the infiltrating group (377.67 ± 21.56 mg) P < 0.05 . The hospitalization time of patients in the infiltrating group (2.14 ± 0.18 days) was obviously shorter than that of the infiltrating group (3.23 ± 0.27 days) P < 0.05 . 3 h, 6 h, and 12 h after the operation, the visual analogue scores (3.82 ± 1.58 points, 2.97 ± 1.53 points, and 1.38 ± 0.57 points) of the patients in the infiltration group were considerably higher than the infiltrating group (2.31 ± 1.46 points, 1.06 ± 1.28 points, and 0.95 ± 0.43 points) P < 0.05 . 3 h, 6 h, and 12 h after the operation, the number of patients in the infiltrating group who used tramadol for salvage analgesia (2 cases, 1 case, and 1 case) was notably less than that in the infiltration group (9 cases, 7 cases, and 3 cases) P < 0.05 . In short, local anesthetics combined with TAP block can reduce postoperative VAS score and postoperative nausea and vomiting (PONV) score, which also reduced the incidence of postoperative analgesia.

scholarly journals Detection and Recognition of Ultrasound Breast Nodules Based on Semi-supervised deep learning

2020 ◽  
Author(s):  
Yanhua Gao ◽  
Bo Liu ◽  
Yuan Zhu ◽  
Lin Chen ◽  
Miao Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Medical Images ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Training Strategy ◽  
Supervised Training ◽  
Malignant Breast

AbstractBackgroundThe successful application of deep learning in medical images requires a large amount of annotation data for supervised training. However, massive labeling of medical data is expensive and time consuming. This paper proposes a semi-supervised deep learning method for the detection and classification of benign and malignant breast nodules in ultrasound images, which include two phases.MethodsThe nodule position in the ultrasound image is firstly detected using the faster RCNN network. Second, the recognition network is used to identify the benign and malignant types of nodules. The method in this paper uses a semi-supervised learning strategy, using 800 labeled nodules and 4396 unlabeled nodules.ResultsBased on mean teacher training strategy, the proposed semi-supervised network has obtained excellent results, which is similar to currently used with supervised training networks. On the two test data sets, the AUC of semi-supervised learning and supervised learning were: 93.7% vs 94.2% and 92% vs 92.3%.ConclusionsThe paper proves that semi-supervised learning strategies have good application potential in medical images. Based on a special learning strategy, the result of semi-supervised learning is expected to achieve close or even achieve similar result of supervised deep learning, which only need a small number of labeled samples and a large number of unlabeled samples. It means deep learning analysis of breast lesion will be more feasible and more efficient.

scholarly journals Automatic Malignant Thyroid Nodule Recognition in Ultrasound Images based on Deep Learning

2020 ◽  
Vol 185 ◽  
pp. 03021
Author(s):  
Meng Zhou ◽  
Rui Wang ◽  
Peng Fu ◽  
Yang Bai ◽  
Ligang Cui
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Data Augmentation ◽  
Medical Training ◽  
Ultrasound Image ◽  
Learning Model ◽  
Training Data ◽  
Ultrasound Images ◽  
Deep Learning Model

As the most common malignancy in the endocrine system, thyroid cancer is usually diagnosed by discriminating the malignant nodules from the benign ones using ultrasonography, whose interpretation results primarily depends on the subjectivity judgement of the radiologists. In this study, we propose a novel cascade deep learning model to achieve automatic objective diagnose during ultrasound examination for assisting radiologists in recognizing benign and malignant thyroid nodules. First, the simplified U-net is employed to segment the region of interesting (ROI) of the thyroid nodules in each frame of the ultrasound image automatically. Then, to alleviate the limitation that medical training data are relatively small in size, the improved Conditional Variational Auto-Encoder (CVAE) learning the probability distribution of ROI images is trained to generate new images for data augmentation. Finally, ResNet50 is trained with both original and generated ROI images. As consequence, the deep learning model formed by the trained U-net and trained Resnet-50 cascade can achieve malignant thyroid nodule recognition with the accuracy of 87.4%, the sensitivity of 92%, and the specificity of 86.8%.

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