Estimation of beef marbling standard for live cattle using multi-input convolutional neural network with ultrasound images

2021 ◽  
Author(s):  
Toshiki Katayama ◽  
Hirohumi Kawada ◽  
Masashi Nishiyama ◽  
Yoshio Iwai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ultrasound Images ◽  
Beef Marbling

Using Convolutional Neural Networks for the Detection of Spinal Transverse Processes

2019 ◽  
Author(s):  
Victoria Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transverse Process ◽  
Classification Model ◽  
Ultrasound Images ◽  
Ultrasound Scan ◽  
X Ray ◽  
Scoliosis Screening

Introduction: Scoliosis, an excessive curvature of the spine, affects approximately 1 in 1,000 individuals. As a result, there have formerly been implementations of mandatory scoliosis screening procedures. Screening programs are no longer widely used as the harms often outweigh the benefits; it causes many adolescents to undergo frequent diagnosis X-ray procedure This makes spinal ultrasounds an ideal substitute for scoliosis screening in patients, as it does not expose them to those levels of radiation. Spinal curvatures can be accurately computed from the location of spinal transverse processes, by measuring the vertebral angle from a reference line [1]. However, ultrasound images are less clear than x-ray images, making it difficult to identify the spinal processes. To overcome this, we employ deep learning using a convolutional neural network, which is a powerful tool for computer vision and image classification [2]. Method: A total of 2,752 ultrasound images were recorded from a spine phantom to train a convolutional neural network. Subsequently, we took another recording of 747 images to be used for testing. All the ultrasound images from the scans were then segmented manually, using the 3D Slicer (www.slicer.org) software. Next, the dataset was fed through a convolutional neural network. The network used was a modified version of GoogLeNet (Inception v1), with 2 linearly stacked inception models. This network was chosen because it provided a balance between accurate performance, and time efficient computations. Results: Deep learning classification using the Inception model achieved an accuracy of 84% for the phantom scan.  Conclusion: The classification model performs with considerable accuracy. Better accuracy needs to be achieved, possibly with more available data and improvements in the classification model.  Acknowledgements: G. Fichtinger is supported as a Canada Research Chair in Computer-Integrated Surgery. This work was funded, in part, by NIH/NIBIB and NIH/NIGMS (via grant 1R01EB021396-01A1 - Slicer+PLUS: Point-of-Care Ultrasound) and by CANARIE’s Research Software Program.    Figure 1: Ultrasound scan containing a transverse process (left), and ultrasound scan containing no transverse process (right).                                Figure 2: Accuracy of classification for training (red) and validation (blue). References:           Ungi T, King F, Kempston M, Keri Z, Lasso A, Mousavi P, Rudan J, Borschneck DP, Fichtinger G. Spinal Curvature Measurement by Tracked Ultrasound Snapshots. Ultrasound in Medicine and Biology, 40(2):447-54, Feb 2014.           Krizhevsky A, Sutskeyer I, Hinton GE. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25:1097-1105. 

A Fully Convolutional Neural Network for Beamforming Ultrasound Images

2018 ◽  
Author(s):  
Arun Asokan Nair ◽  
Mardava Rajugopal Gubbi ◽  
Trac Duy Tran ◽  
Austin Reiter ◽  
Muyinatu A. Lediju Bell
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ultrasound Images

Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients

2020 ◽  
Vol 79 (9) ◽  
pp. 1189-1193
Author(s):  
Anders Bossel Holst Christensen ◽  
Søren Andreas Just ◽  
Jakob Kristian Holm Andersen ◽  
Thiusius Rajeeth Savarimuthu
Keyword(s):  
Neural Network ◽  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Convolutional Neural Network ◽  
Objective Assessment ◽  
Rank Test ◽  
Ultrasound Images ◽  
Data Set ◽  
Unseen Data ◽  
Significant Difference

ObjectivesWe have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheumatoid arthritis (RA) patients, giving scores according to the EULAR-OMERACT grading system. We have now further developed the architecture of this neural network and can here present a new idea applying cascaded convolutional neural network (CNN) design with even better results. We evaluate the generalisability of this method on unseen data, comparing the CNN with an expert rheumatologist.MethodsThe images were graded by an expert rheumatologist according to the EULAR-OMERACT synovitis scoring system. CNNs were systematically trained to find the best configuration. The algorithms were evaluated on a separate test data set and compared with the gradings of an expert rheumatologist on a per-joint basis using a Kappa statistic, and on a per-patient basis using a Wilcoxon signed-rank test.ResultsWith 1678 images available for training and 322 images for testing the model, it achieved an overall four-class accuracy of 83.9%. On a per-patient level, there was no significant difference between the classifications of the model and of a human expert (p=0.85). Our original CNN had a four-class accuracy of 75.0%.ConclusionsUsing a new network architecture we have further enhanced the algorithm and have shown strong agreement with an expert rheumatologist on a per-joint basis and on a per-patient basis. This emphasises the potential of using CNNs with this architecture as a strong assistive tool for the objective assessment of disease activity of RA patients.

Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network

2020 ◽  
Vol 65 (3) ◽  
pp. 035016 ◽  
Author(s):  
Maryam Golshan ◽  
Davood Karimi ◽  
Sara Mahdavi ◽  
Julio Lobo ◽  
Michael Peacock ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
3D Ultrasound ◽  
Ultrasound Images

Segmentation and Measurements of Carotid Intima-Media Thickness in Ultrasound Images Using the Improved Convolutional Neural Network and Support Vector Machine

2021 ◽  
Vol 11 (1) ◽  
pp. 15-24
Author(s):  
Dequan Guo ◽  
Gexiang Zhang ◽  
Hui Peng ◽  
Jianying Yuan ◽  
Prithwineel Paul ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Intima Media Thickness ◽  
Ground Truth ◽  
Classification Model ◽  
Support Vector ◽  
Ultrasound Images ◽  
Boundary Points ◽  
Media Thickness

In recent years, diseases of cardiovascular and cerebrovascular have attracted much attention due to main causes in death in human beings. To reduce mortality, there are lots of efforts which are focused on early diagnosis and prevention. It is an important reference index for cardiovascular diseases through the endovascular membrane in carotid artery by medical ultrasound images. The paper proposes a method which finds the region of interest (ROI) by convolutional neural network, segments and measures intima-media membrane mainly using support vector machine (SVM). Essentially, the task of detecting the membrane is one target detection problem. This paper adopts the strategy, named Yon Only Look Once (YOLO), a new detection algorithm, and follows the convolution neural network algorithm based on end-to-end training. Firstly, sufficient samples are extracted according to certain characteristics in the special region. It can be trained by the SVM classification model. Then the ROI is processed and all the pixels are classified into boundary points and non-boundary points through the classification model. Thirdly, the boundary points are selected to obtain the accurate boundary and calculate the intima-media thickness (IMT). In experiments, two hundred ultrasound images are tested, and the results verify that our algorithm is consistent with the results by ground truth (GT). The detection speed of the algorithm in this paper is in real time, and it has high generalization characteristics. The algorithm computes the intima-media thickness in ultrasound images accurately and quickly with 95% consistence to ground truth.

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