Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images

Nguyễn, Hải Thanh; Tran, Toan; Luong, Huong Hoang; Huynh, Tuan Khoi Nguyen

doi:10.7717/peerj-cs.719

Cited by 8 publications

(5 citation statements)

References 43 publications

(59 reference statements)

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“…It can achieve accurate pixel-level segmentation with less data. Thus, UNet++ is widely applied in the field of medical image segmentation, such as Bacillus anthracis [ 6 ], leukocytes [ 13 ], COVID-19 [ 15 ]. This study did not use the method of data enhancement, randomly split the data set for training, the results showed that the average IoU can reach 95.41%.…”

Section: Discussionmentioning

confidence: 99%

Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis

et al. 2022

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Purpose Current research on the aging of bony orbit is usually done manually, which is inefficient and has a large error. In this paper, automatic segmentation of bony orbit based on deep learning and automatic calculation of the parameters of the segmented orbital contour (area and height of bony orbit) are presented. Methods The craniofacial CT scanning data of 595 Chinese were used to carry out three-dimensional reconstruction and output the craniofacial images. The orbital contour images are obtained automatically by UNet++ segmentation network, and then the bony orbital area and height were calculated automatically by connected component analysis. Results The automatic segmentation method has an Intersection of Union of 95.41% in craniofacial CT images. During the aging, the bony orbital area of males increased with age, while that of females decreased, and the area in male was larger than that in female ( P < 0.05). The distance from equal points 10 and 40–90 to the supraorbital rim was significantly larger ( P < 0.05). Except for the equal point 90, the distance from equal points to the inferior orbital rim was obviously larger ( P < 0.05). In the females, the distance from equal points 50–70 to inferior orbital rim was significantly lower ( P < 0.05). Conclusion The method proposed here can automatically and accurately study image dataset of large-scale bony orbital CT imaging. UNet++ can achieve high-precision segmentation of bony orbital contours. The bony orbital area of Chinese changes with aging, and the bony orbital height changes different between males and females, which may be caused by the different position and degree of orbital bone resorption of males and females in the process of aging.

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Section: Discussionmentioning

confidence: 99%

Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis

et al. 2022

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“…Semantic segmentation (SS) is an essential component in image processing and computer vision. SS has multiple applications, such as scene understanding ( Hofmarcher et al, 2019 ), autonomous driving ( Feng et al, 2020 ), and medical image analysis ( Liu et al, 2020 ; Abedalla et al, 2021 ; Nguyen et al, 2021 ), among many others. The goal of SS is to change the representation of an image into something more meaningful and easier to analyze using post-processing algorithms.…”

Section: Methodsmentioning

confidence: 99%

Use of semantic segmentation for mapping Sargassum on beaches

Arellano‐Verdejo

Santos-Romero²,

Lazcano-Hernandez

2022

PeerJ

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The unusual arrival of Sargassum on Caribbean beaches is an emerging problem that has generated numerous challenges. The monitoring, visualization, and estimation of Sargassum coverage on the beaches remain a constant complication. This study proposes a new mapping methodology to estimate Sargassum coverage on the beaches. Semantic segmentation of geotagged photographs allows the generation of accurate maps showing the percent coverage of Sargassum. The first dataset of segmented Sargassum images was built for this study and used to train the proposed model. The results demonstrate that the currently proposed method has an accuracy of 91%, improving on the results reported in the state-of-the-art method where data was also collected through a crowdsourcing scheme, in which only information on the presence and absence of Sargassum is displayed.

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“…The traditional semantic segmentation approaches that utilize this architectural style are SegNet ( Badrinarayanan, Kendall & Cipolla, 2017 ), U-Net ( Ronneberger, Fischer & Brox, 2015 ; Nguyen et al, 2021 ), DeepLab ( Chen et al, 2017 ), etc . The encoder element in this architecture consists of the pre-trained classification network that extracts the compound semantic features.…”

Section: Related Workmentioning

confidence: 99%

Wall segmentation in 2D images using convolutional neural networks

Bjekic,

Lazovic,

et al. 2023

PeerJ Computer Science

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Wall segmentation is a special case of semantic segmentation, and the task is to classify each pixel into one of two classes: wall and no-wall. The segmentation model returns a mask showing where objects like windows and furniture are located, as well as walls. This article proposes the module’s structure for semantic segmentation of walls in 2D images, which can effectively address the problem of wall segmentation. The proposed model achieved higher accuracy and faster execution than other solutions. An encoder-decoder architecture of the segmentation module was used. Dilated ResNet50/101 network was used as an encoder, representing ResNet50/101 network in which dilated convolutional layers replaced the last convolutional layers. The ADE20K dataset subset containing only interior images, was used for model training, while only its subset was used for model evaluation. Three different approaches to model training were analyzed in the research. On the validation dataset, the best approach based on the proposed structure with the ResNet101 network resulted in an average accuracy at the pixel level of 92.13% and an intersection over union (IoU) of 72.58%. Moreover, all proposed approaches can be applied to recognize other objects in the image to solve specific tasks.

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Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images

Cited by 8 publications

References 43 publications

Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis

Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis

Use of semantic segmentation for mapping Sargassum on beaches

Wall segmentation in 2D images using convolutional neural networks

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