Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Wu, Wenbin; Liu, Guanjun; Liang, Kaiyi; Zhou, Hui

doi:10.32604/cmes.2022.020428

Cited by 3 publications

(3 citation statements)

References 29 publications

(41 reference statements)

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“…On the other hand, U 2 -Net uses two nested U-Net models. It can mine more information from the data and fuse the information at the network level to achieve better segmentation results (Orlando et al 2019, Wu et al 2023.…”

Section: Discussionmentioning

confidence: 99%

Automatic brain extraction for rat magnetic resonance imaging data using U²-Net

Liang,

Yin,

Huang

et al. 2023

Phys. Med. Biol.

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Objective. Skull stripping is a key step in the pre-processing of rodent brain magnetic resonance images (MRI). This study aimed to develop a new skull stripping method via U2-Net, a neural network model based on deep learning method, for rat brain magnetic resonance images.Approach. In this study, 599 rats were enrolled and U2-Net was applied to segment MRI images of rat brain. The intercranial tissue of each rat was manually labeled. 476 rats (approximate 80%) were used for training set while 123 rats (approximate 20%) were used to test the performance of the trained U2-Net model. For evaluation, the segmentation result by the U2-Net model is compared with the manual label, and traditional segment methods. Quantitative evaluation, including Dice coefficient, Jaccard coefficient, Sensitivity, Specificity, Pixel accuracy, Hausdorff coefficient, True positive rate, False positive rate and the volumes of whole brain, were calculated to compare the segmentation results among different models.Main results. The U2-Net model was performed better than the software of RATS and BrainSuite, in which the quantitative values of training U2-Net model were 0.9907±0.0016(Dice coefficient), 0.9816±0.0032(Jaccard coefficient), 0.9912±0.0020(Sensitivity), 0.9989±0.0002(Specificity), 0.9982±0.0003( Pixel accuracy), 5.2390±2.5334(Hausdorff coefficient), 0.9902±0.0025(True positive rate), 0.0009±0.0002(False positive rate) respectively. Significance. This study provides a new method that achieves reliable performance in rat brain skull stripping of MRI images, which could contribute to the processing of rat brain magnetic resonance images.

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Section: Discussionmentioning

confidence: 99%

Automatic brain extraction for rat magnetic resonance imaging data using U²-Net

Liang,

Yin,

Huang

et al. 2023

Phys. Med. Biol.

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“…Wu et al [96] proposed a novel CNN-based self-supervised method to simultaneously attenuate seismic random noise and offset artifacts, called a self-adaptive denoising network (SaDN), which uses the assumption that synthetic noise with mixed Gaussian-Poisson distribution can simulate random noise and migration artifacts to modify the loss function of a denoising CNN (DnCNN) to adapt to different field data. Mandelli et al [97] investigated a specific architecture based on convolutional neural networks, called U-Net [98,99], and implemented a convolutional self-encoder, which can reconstruct more complex seismic data more efficiently and accurately compared to traditional CNN models. To improve the performance of the U-Net.…”

Section: Seismic Data Interpolation and Denoisingmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

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“…Furthermore, deep learning has opened up new possibilities for researchers in various fields, including target recognition. Wu et al (2023) devised novel Inner Cascaded U-Net and Inner Cascaded U2-Net as improvements to plain cascaded U-Net for medical image segmentation, achieving better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as getting finer outline segmentation. Nie et al (2022) proposed a method named SegNet that was developed and trained with different data groups.…”

Section: Introductionmentioning

confidence: 99%

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

Zhang,

Sun,

Xie

et al. 2023

Front. Plant Sci.

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IntroductionThe identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.MethodsTo address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.Results and discussionThe MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking.

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Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Cited by 3 publications

References 29 publications

Automatic brain extraction for rat magnetic resonance imaging data using U²-Net

Automatic brain extraction for rat magnetic resonance imaging data using U²-Net

Recent Advances of Deep Learning in Geological Hazard Forecasting

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

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Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Cited by 3 publications

References 29 publications

Automatic brain extraction for rat magnetic resonance imaging data using U2-Net

Automatic brain extraction for rat magnetic resonance imaging data using U2-Net

Recent Advances of Deep Learning in Geological Hazard Forecasting

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

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Automatic brain extraction for rat magnetic resonance imaging data using U²-Net

Automatic brain extraction for rat magnetic resonance imaging data using U²-Net