scholarly journals Error-Correcting Neural Networks for Semi-Lagrangian Advection in the Level-Set Method

2021 ◽  
Author(s):  
Luis Ángel Larios-Cárdenas ◽  
Frederic Gibou
Keyword(s):  
Neural Networks ◽  
Level Set Method ◽  
Level Set ◽  
Lagrangian Advection

Automatic object extraction from images using deep neural networks and the level-set method

2018 ◽  
Vol 12 (7) ◽  
pp. 1131-1141 ◽  
Author(s):  
Kan Wu ◽  
Yizhou Yu
Keyword(s):  
Neural Networks ◽  
Level Set Method ◽  
Level Set ◽  
Deep Neural Networks ◽  
Object Extraction

Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

2021 ◽  
Vol 419 ◽  
pp. 108-125
Author(s):  
Yunyun Yang ◽  
Ruicheng Xie ◽  
Wenjing Jia ◽  
Zhaoyang Chen ◽  
Yunna Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Level Set Method ◽  
Convolutional Neural Networks ◽  
Level Set ◽  
Deep Convolutional Neural Networks

scholarly journals NEURAL NETWORK AND CONVOLUTIONAL ALGORITH TO EXTRACT SHAPES BY E-MEDICUS APPLICATION

2018 ◽  
Vol 8 (3) ◽  
pp. 39-42
Author(s):  
Tomasz Rymarczyk ◽  
Barbara Stefaniak ◽  
Przemysław Adamkiewicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Level Set Method ◽  
Level Set ◽  
Arbitrary Number ◽  
Medical Images ◽  
Learning Methods ◽  
Segmentation Problem

The solution shows the architecture of the system collecting and analyzing data. There was tried to develop algorithms to image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. With the use of algorithms such as the level set method, neural networks and deep learning methods, it can obtain a quicker diagnosis and automatically marking areas of the interest region in medical images.

scholarly journals Learning High-Order Geometric Flow Based on The Level Set Method

2021 ◽  
Author(s):  
Chun Li ◽  
Yunyun Yang ◽  
Hui Liang ◽  
Boying Wu
Keyword(s):  
Neural Networks ◽  
Level Set Method ◽  
Level Set ◽  
Input Data ◽  
Initial Conditions ◽  
High Order ◽  
Geometric Flow ◽  
Set Up ◽  
Almost All ◽  
Physical Phenomena

Abstract Recently, the development of deep learning (DL), which has accomplished unbelievable success in many fields, especially in scientific computational fields. And almost all computational problems and physical phenomena can be described by partial differential equations (PDEs). In this work, we proposed two potential high-order geometric flows. Motivation by the physical-information neural networks (PINNs) and the traditional level set method (LSM), we have integrated deep neural networks (DNNs) and LSM to make the proposed method more robust and efficient. Also, to test the sensitivity of the system to different input data, we set up three sets of initial conditions to test the model. Furthermore, numerical experiments on different input data are implemented to demonstrate the effectiveness and superiority of the proposed models compared to the state-of-the-art approach.

Level Set Method Optimized with the Euclidean Distance Transform

2019 ◽  
Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform
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