|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM352234377 |
| 003 |
DE-627 |
| 005 |
20250304090343.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1002/ima.22819
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1173.xml
|
| 035 |
|
|
|a (DE-627)NLM352234377
|
| 035 |
|
|
|a (NLM)36713026
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Ma, Lu
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet
|
| 264 |
|
1 |
|c 2023
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 11.09.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a © 2022 Wiley Periodicals LLC.
|
| 520 |
|
|
|a Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https://github.com/malu108/LungInfectionSeg
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a COVID‐19
|
| 650 |
|
4 |
|a area loss function
|
| 650 |
|
4 |
|a computed tomography (CT) image segmentation
|
| 650 |
|
4 |
|a deep learning
|
| 650 |
|
4 |
|a pre‐activated residual block
|
| 700 |
1 |
|
|a Song, Shuni
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Guo, Liting
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tan, Wenjun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Xu, Lisheng
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t International journal of imaging systems and technology
|d 1990
|g 33(2023), 1 vom: 21. Jan., Seite 6-17
|w (DE-627)NLM098193090
|x 0899-9457
|7 nnas
|
| 773 |
1 |
8 |
|g volume:33
|g year:2023
|g number:1
|g day:21
|g month:01
|g pages:6-17
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1002/ima.22819
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 33
|j 2023
|e 1
|b 21
|c 01
|h 6-17
|