Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs

2020 ◽  
Vol 17 (11) ◽  
pp. 1475-1484
Author(s):  
Alexander Goehler ◽  
Tzu-Ming Harry Hsu ◽  
Ronilda Lacson ◽  
Isha Gujrathi ◽  
Raein Hashemi ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Tumor ◽  
Three Dimensional ◽  
Tumor Burden

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Automatic method for glaucoma diagnosis using a three-dimensional convoluted neural network

2021 ◽  
Vol 438 ◽  
pp. 72-83
Author(s):  
Nonato Rodrigues de Sales Carvalho ◽  
Maria da Conceição Leal Carvalho Rodrigues ◽  
Antonio Oseas de Carvalho Filho ◽  
Mano Joseph Mathew
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Automatic Method ◽  
Glaucoma Diagnosis

scholarly journals The Role of Serum 5-HIAA as a Predictor of Progression and an Alternative to 24-h Urine 5-HIAA in Well-Differentiated Neuroendocrine Neoplasms

Biology ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 76
Author(s):  
Maria Wedin ◽  
Sagar Mehta ◽  
Jenny Angerås-Kraftling ◽  
Göran Wallin ◽  
Kosmas Daskalakis
Keyword(s):  
Liver Tumor ◽  
Disease Surveillance ◽  
Tumor Burden ◽  
Neuroendocrine Neoplasms ◽  
Mri Imaging ◽  
Exact Test ◽  
Recist 1.1 ◽  
Tumor Involvement ◽  
Well Differentiated ◽  
Disease Stages

Our aim was to investigate the clinical utility of serum 5HIAA for disease surveillance and diagnostic purposes in a cohort of patients with well-differentiated neuroendocrine neoplasms (WD-NENs). Forty-eight patients with WD-NENs and concurrent serum and urinary 5HIAA testing, as well as CT/MRI imaging, were included. Analysis of matching-pairs did not reveal any association between RECIST 1.1 responses and changes in serum 5HIAA levels (p = 0.673). In addition, no correlation was evident between RECIST 1.1 responses and >10%, >25% or >50% changes in serum 5HIAA levels (Fisher’s exact test p = 0.380, p > 0.999, and p > 0.999, respectively). The presence of liver metastases and extensive liver tumor involvement were associated with higher serum 5HIAA levels (p = 0.045 and p = 0.041, respectively). We also confirmed a strong linear correlation between the measurements of serum and urine 5HIAA (n = 24, r = 0.791, p < 0.0001). The concordance rate of serum and urinary 5HIAA positivity at standardized laboratory cut-offs was 75%. In patients with normal renal function tests, the concordance between the two methods was as high as 89%, and a sensitivity and specificity of 80% and 88.9%, respectively, was evident (Cohen’s kappa coefficient = 0.685). In conclusion, serum 5HIAA performs well compared to urinary testing for diagnostic purposes, mainly in advanced disease stages, and corresponds well to liver tumor burden. However, it is not adequate to predict tumor progression.

scholarly journals Estimation of 6D Object Pose Using a 2D Bounding Box

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2939
Author(s):  
Yong Hong ◽  
Jin Liu ◽  
Zahid Jahangir ◽  
Sheng He ◽  
Qing Zhang
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
Three Dimensional ◽  
Unit Vector ◽  
Prediction Algorithm ◽  
Computational Time ◽  
Bounding Box ◽  
Dimensional Unit ◽  
Bounding Boxes ◽  
Rgb Image

This paper provides an efficient way of addressing the problem of detecting or estimating the 6-Dimensional (6D) pose of objects from an RGB image. A quaternion is used to define an object′s three-dimensional pose, but the pose represented by q and the pose represented by -q are equivalent, and the L2 loss between them is very large. Therefore, we define a new quaternion pose loss function to solve this problem. Based on this, we designed a new convolutional neural network named Q-Net to estimate an object’s pose. Considering that the quaternion′s output is a unit vector, a normalization layer is added in Q-Net to hold the output of pose on a four-dimensional unit sphere. We propose a new algorithm, called the Bounding Box Equation, to obtain 3D translation quickly and effectively from 2D bounding boxes. The algorithm uses an entirely new way of assessing the 3D rotation (R) and 3D translation rotation (t) in only one RGB image. This method can upgrade any traditional 2D-box prediction algorithm to a 3D prediction model. We evaluated our model using the LineMod dataset, and experiments have shown that our methodology is more acceptable and efficient in terms of L2 loss and computational time.

Finding Features of Positioning Error for Large Industrial Robots Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Daiki Kato ◽  
Kenya Yoshitugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production Systems ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Numerical Control ◽  
Robot Motion ◽  
Positioning Error ◽  
End Effector

Abstract Most industrial robots are taught using the teaching playback method; therefore, they are unsuitable for use in variable production systems. Although offline teaching methods have been developed, they have not been practiced because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have attempted to calibrate the position and posture but have not reached a practical level, as such methods consider the joint angle when the robot is stationary rather than the features during robot motion. Currently, it is easy to obtain servo information under numerical control operations owing to the Internet of Things technologies. In this study, we propose a method for obtaining servo information during robot motion and converting it into images to find features using a convolutional neural network (CNN). Herein, a large industrial robot was used. The three-dimensional coordinates of the end-effector were obtained using a laser tracker. The positioning error of the robot was accurately learned by the CNN. We extracted the features of the points where the positioning error was extremely large. By extracting the features of the X-axis positioning error using the CNN, the joint 1 current is a feature. This indicates that the vibration current in joint 1 is a factor in the X-axis positioning error.

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