About: COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
Creator	Zhang, Liang Zhao, Wei Wang, Bo Zhang, L Wang, B Zhao, W Jin, S Jin, Shuo Luo, Chuan You, Zheng Wang, ) Gong, Dong Shen, J Shen, Jianhu Shi, Qinfeng Yan, Q Yan, Qingsen You, Z
Source	ArXiv
abstract	A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We firstly maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively.
has issue date	2020-04-23(xsd:dateTime)
has license	arxiv
sha1sum (hex)	34e105872c774ec5f4d8c06f0c1b80b370cdc111
resource representing a document's title	COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
resource representing a document's body	covid:34e105872c774ec5f4d8c06f0c1b80b370cdc111#body_text
is schema:about of	named entity 'infection' named entity 'automatic' named entity 'suspected' named entity 'enhanced' named entity 'performance' named entity 'collected' named entity 'confirmed' named entity 'chest' named entity 'IMAGE SEGMENTATION' named entity 'DEEP' named entity 'images' named entity 'enhance' named entity 'features' named entity 'manual' named entity 'rapidly' named entity 'Inspired' named entity 'coronavirus disease 2019' named entity 'lung' named entity 'CT image' named entity 'proposed' named entity 'infection' named entity 'imaging' named entity 'coefficients' named entity 'COVID-19' named entity 'convolutional neural network' named entity 'lung' named entity 'chest CT' named entity 'COVID-19' named entity 'COVID-19 infection' named entity 'chest CT' named entity 'Deep Convolutional Neural Network' named entity 'Dice' named entity 'COVID' named entity 'COVID' named entity 'chest radiography' named entity 'Germany' named entity 'convolutional layer' named entity 'deep learning' named entity 'China.' named entity 'COVID' named entity '1/2' named entity 'COVID' named entity 'convolutional layer' named entity 'lung' named entity 'COVID' named entity 'COVID' named entity 'loss function' named entity 'deep learning' named entity 'COVID' named entity 'convolutional layer' named entity 'ASPP' named entity 'deep convolutional neural network' named entity 'Lung' named entity 'Reverse transcription polymerase chain reaction' named entity 'chest CT' named entity 'COVID-19' named entity 'infection' named entity 'VNet' named entity 'chest CT' named entity 'COVID' named entity 'lung' named entity 'COVID' named entity 'sigmoid function' named entity 'COVID' named entity 'infection' named entity 'COVID-19' named entity 'receptive field' named entity 'Brainlab' named entity 'febrile' named entity 'CNN'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software