Cargando…

SCTV-UNet: a COVID-19 CT segmentation network based on attention mechanism

The global outbreak of COVID-19 has become an important research topic in healthcare since 2019. RT-PCR is the main method for detecting COVID-19, but the long detection time is a problem. Therefore, the pathological study of COVID-19 with CT image is an important supplement to RT-RCT. The current T...

Descripción completa

Detalles Bibliográficos
Autores principales:	Liu, Xiangbin, Liu, Ying, Fu, Weina, Liu, Shuai
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2023
Materias:	Focus
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10028784/ https://www.ncbi.nlm.nih.gov/pubmed/37362261 http://dx.doi.org/10.1007/s00500-023-07991-7

Ejemplares similares

PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans
por: Bougourzi, Fares, et al.
Publicado: (2023)

SAA-UNet: Spatial Attention and Attention Gate UNet for COVID-19 Pneumonia Segmentation from Computed Tomography
por: Alshomrani, Shroog, et al.
Publicado: (2023)

COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet
por: Saeedizadeh, Narges, et al.
Publicado: (2021)

Improved UNet with Attention for Medical Image Segmentation
por: AL Qurri, Ahmed, et al.
Publicado: (2023)

MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images
por: Chi, Jianning, et al.
Publicado: (2022)

TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation
por: Huang, Xuping, et al.
Publicado: (2022)

Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images
por: Nguyen, Hai Thanh, et al.
Publicado: (2021)

PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation
por: Chen, Danny, et al.
Publicado: (2022)

A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images
por: He, Hongliang, et al.
Publicado: (2021)

Cross-Attention and Deep Supervision UNet for Lesion Segmentation of Chronic Stroke
por: Sheng, Manjin, et al.
Publicado: (2022)

ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans
por: Joseph Raj, Alex Noel, et al.
Publicado: (2021)

P-TransUNet: an improved parallel network for medical image segmentation
por: Chong, Yanwen, et al.
Publicado: (2023)

TA-Unet: Integrating Triplet Attention Module for Drivable Road Region Segmentation
por: Li, Sijia, et al.
Publicado: (2022)

DSKCA-UNet: Dynamic selective kernel channel attention for medical image segmentation
por: Shen, Longfeng, et al.
Publicado: (2023)

Unfolded deep kernel estimation-attention UNet-based retinal image segmentation
por: Radha, K., et al.
Publicado: (2023)

Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume
por: L., Aswathy A., et al.
Publicado: (2022)

Anterior Mediastinal Lesion Segmentation Based on Two-Stage 3D ResUNet With Attention Gates and Lung Segmentation
por: Huang, Su, et al.
Publicado: (2021)

nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation
por: Zhao, Li, et al.
Publicado: (2022)

RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
por: Jin, Qiangguo, et al.
Publicado: (2020)

MAS-UNet: a U-shaped network for prostate segmentation
por: Hong, YuQi, et al.
Publicado: (2023)

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN
por: Jiang, Yun, et al.
Publicado: (2022)

A New Semantic Segmentation Framework Based on UNet
por: Fu, Leiyang, et al.
Publicado: (2023)

Application of a pyramid pooling Unet model with integrated attention mechanism and Inception module in pancreatic tumor segmentation
por: Zhang, Zhiwei, et al.
Publicado: (2023)

TMD-Unet: Triple-Unet with Multi-Scale Input Features and Dense Skip Connection for Medical Image Segmentation
por: Tran, Song-Toan, et al.
Publicado: (2021)

SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image
por: Zhang, Xiang, et al.
Publicado: (2022)

Ore image segmentation method using U-Net and Res_Unet convolutional networks
por: Liu, Xiaobo, et al.
Publicado: (2020)

Fault Recognition Method Based on Attention Mechanism and the 3D-UNet
por: Yu, Ting, et al.
Publicado: (2022)

Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet
por: Özcan, Fırat, et al.
Publicado: (2023)

LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation
por: Zhang, Shuai, et al.
Publicado: (2023)

Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation
por: Chen, Shaolong, et al.
Publicado: (2022)

Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet
por: Khaledyan, Donya, et al.
Publicado: (2023)

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model
por: Shamim, Sania, et al.
Publicado: (2022)

SigUNet: signal peptide recognition based on semantic segmentation
por: Wu, Jhe-Ming, et al.
Publicado: (2019)

New hybrid segmentation algorithm: UNet-GOA
por: Yousefi, Tohid, et al.
Publicado: (2023)

C-UNet: Complement UNet for Remote Sensing Road Extraction
por: Hou, Yuewu, et al.
Publicado: (2021)

A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution
por: Safarov, Sirojbek, et al.
Publicado: (2021)

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images
por: Chen, Cheng, et al.
Publicado: (2021)

RatUNet: residual U-Net based on attention mechanism for image denoising
por: Zhang, Huibin, et al.
Publicado: (2022)

A Residual UNet Denoising Network Based on Multi-Scale Feature Extraction and Attention-Guided Filter
por: Liu, Hualin, et al.
Publicado: (2023)

Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography
por: Katakis, Sofoklis, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_ge0l3qq1h105qqsc272n22utkh