Non-rigid registration of biomedical image for radiotherapy based on adaptive feature density flow

2021 ◽  
Vol 68 ◽  
pp. 102691
Author(s):  
Jinghua Xu ◽  
Mingzhe Tao ◽  
Shuyou Zhang ◽  
Xue Jiang ◽  
Jianrong Tan
Keyword(s):  
Rigid Registration ◽  
Biomedical Image ◽  
Density Flow

INTRODUCING TENSOR ANALYSIS FOR BIOMEDICAL IMAGE REGISTRATION

2018 ◽  
Author(s):  
Wilian Fiirst ◽  
José Montero ◽  
ROGER RESMINI ◽  
Anselmo Antunes Montenegro ◽  
Trueman McHenry ◽  
...  
Keyword(s):  
Image Registration ◽  
Tensor Analysis ◽  
Biomedical Image ◽  
Biomedical Image Registration

Study of the fractional composition of granulate influence on the performance indicators of tablets based on salmonellosis bacteriophag

2020 ◽  
pp. 74-77
Keyword(s):  
Performance Indicators ◽  
Fractional Composition ◽  
Pressing Pressure ◽  
Technological Properties ◽  
Compression Pressure ◽  
Plastic Properties ◽  
Technological Factors ◽  
Density Flow ◽  
Crush Strength ◽  
Quality Indexes

The author presented experimental data on the influence of technological factors on the quality indexes of tablets based on a cryoliofilized salmonella bacteriophage. The analysis of the technological properties of model granules with such parameters as bulk density, flow ability, vibration compacting index, Hausner index, Сarr's index showed that the fractional composition of the granules should contain no more than 33 % of the pulverized fraction (0,25 mm or less). The granulate, regardless of its fractional composition, has elastic-plastic properties characterizing the strength of the tablets. The influence of the pressing pressure of the cryoliofilized composition tablets with salmonella bacteriophage on the crush strength and disintegration of enteric-soluble tablets was studied. The optimum compression pressure of tablets within 60 mPa of granules with a content of pulp fraction (0.25 mm or less) is not to be higher than 33 %.

Research on the Topology Preservation of the Demons Non-rigid Registration Algorithm

2010 ◽  
Vol 36 (1) ◽  
pp. 179-183
Author(s):  
Xiang-Bo LIN ◽  
Tian-Shuang QIU ◽  
Su RUAN ◽  
NICOLIER Frédéric
Keyword(s):  
Topology Preservation ◽  
Rigid Registration ◽  
Registration Algorithm

scholarly journals Magnetospheric Structure of Rotation-Powered Neutron Stars

1992 ◽  
Vol 128 ◽  
pp. 56-77 ◽  
Author(s):  
Jonathan Arons
Keyword(s):  
Radio Emission ◽  
Gamma Radiation ◽  
Heavy Ions ◽  
Theoretical Work ◽  
Future Research ◽  
Observational Constraints ◽  
Polar Caps ◽  
Recent Theoretical Work ◽  
Density Flow ◽  
Promising Avenue

AbstractI survey recent theoretical work on the structure of the magnetospheres of rotation-powered pulsars, within the observational constraints set by their observed spindown, their ability to power synchrotron nebulae and their ability to produce beamed collective radio emission, while putting only a small fraction of their energy into incoherent X- and gamma radiation. I find no single theory has yet given a consistent description of the magnetosphere, but I conclude that models based on a dense outflow of pairs from the polar caps, permeated by a lower density flow of heavy ions, are the most promising avenue for future research.

scholarly journals Designing high energy density flow batteries by tuning active-material thermodynamics

RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5432-5443
Author(s):  
Shyam K. Pahari ◽  
Tugba Ceren Gokoglan ◽  
Benjoe Rey B. Visayas ◽  
Jennifer Woehl ◽  
James A. Golen ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Density ◽  
Fossil Fuels ◽  
Active Material ◽  
High Energy ◽  
Theoretical Framework ◽  
High Energy Density ◽  
Density Flow ◽  
The Cost

With the cost of renewable energy near parity with fossil fuels, energy storage is paramount. We report a breakthrough on a bioinspired NRFB active-material, with greatly improved solubility, and place it in a predictive theoretical framework.

SEAUNet:Domain Adaptation for Biomedical Image Segmentation

2020 ◽  
Author(s):  
Hang Chen ◽  
Leiting Chen ◽  
Yuchu Chen ◽  
Minghao Fan ◽  
Chuan Zhou
Keyword(s):  
Image Segmentation ◽  
Biomedical Image

scholarly journals Unsupervised content-preserving transformation for optical microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinyang Li ◽  
Guoxun Zhang ◽  
Hui Qiao ◽  
Feng Bao ◽  
Yue Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Training Data ◽  
Fluorescence Labeling ◽  
Imaging Data ◽  
Image Transformation ◽  
General Applicability ◽  
Data Annotation ◽  
Biomedical Image ◽  
Wide Range

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

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