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An Efficient DA-Net Architecture for Lung Nodule Segmentation
2021
Mathematics
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Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. ...
Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. ...
Our proposed framework follows an end-to-end approach for the segmentation of lung nodules. A U-Net is an efficient segmentation algorithm in biomedical image segmentation. ...
doi:10.3390/math9131457
fatcat:3ohogfm74zdjhpqptzm5i3wmra
A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks
2020
Sensors
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The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT ...
Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the "suspicious nodules" in the image. ...
Dilated-Convolution U-Net for Nodule Candidate Detection Taking the image with outside-lung regions filtered out as input, we propose another U-Net-like network to detect nodule candidates. ...
doi:10.3390/s20154301
pmid:32752225
pmcid:PMC7435753
fatcat:6yc2y5zxqrfslo2aylpomztma4
ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation
2021
Sensors
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Finally, a densely connected convolutional layer is utilized for the contracting path. ...
Lung CT image segmentation is a key process in many applications such as lung cancer detection. ...
[37] , using the idea of the FCN, proposed U-shape Net (U-Net) framework for biomedical image segmentation. U-Net is one of the most popular FCNs for segmentation of medical images. ...
doi:10.3390/s21010268
pmid:33401581
fatcat:knfpjxnwhjcvnp3ur45iusbtpi
Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification
2020
Medical Physics (Lancaster)
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Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. ...
This demonstrates the effectiveness of our developed CAD system for lung nodule detection. ...
We used a convolutional neural network model, U-net++, to detect potential nodule candidates on axial, coronal, and sagittal planes. ...
doi:10.1002/mp.14648
pmid:33300162
fatcat:jbotre3gwbdpvh7svj2fun74ea
Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review
2022
Diagnostics
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The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. ...
Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. ...
U-NET network LUNA16 U-Net Modify residual block DSC = 73.6 Obtain lung parenchyma 3D 3D U-Net and 3D U-Net Zhao et al. [60] 2018 Contextual Convolutional LIDC-IDRI GAN Morphological methods None Neural ...
doi:10.3390/diagnostics12020298
pmid:35204388
pmcid:PMC8871398
fatcat:zbasqznr5vblnkfmeuzwlmqbom
A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules based on Computed Tomography Scans
2020
IEEE Access
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The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics, and various medical competitions. ...
Due to the extensive use of Convolutional Neural Network (CNN)based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. ...
[68] developed a Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), which use different ways of concatenation to take full advantages of multiple feature maps for lung nodule ...
doi:10.1109/access.2020.3018666
fatcat:efatgjz7srg5vjfqrx75ttyzbu
Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images
[article]
2019
arXiv
pre-print
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convolution into two simpler operations; (ii) dilated residual dense blocks to efficiently expand the receptive field of the network and aggregate multi-scale contextual information for segmentation; and ...
The PLS-Net is based on an asymmetric encoder-decoder architecture with three novel components: (i) 3D depthwise separable convolutions to improve the network efficiency by factorising each regular 3D ...
There was no statistically significant difference between the overall performance of the other 3D FCN-based methods (FRV-Net, 3D U-Net and PDV-Net) (p > 0.05); however, the PDV-Net, which also uses densely-connected ...
arXiv:1909.07474v1
fatcat:awf6lcrktrh5dj4hkpgkmoj5zy
D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism
[article]
2021
arXiv
pre-print
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In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues ...
Besides, we also build a simplified D2A U-Net without pretrained encoder to provide a fair comparison with other models trained from scratch, which still outperforms popular U-Net family models with a ...
Acknowledgements This work was partially supported by the Fundamental Research Funds for Central Universities, the National Natural Science Foundation of China (No. 61601019, 61871022) , the Beijing Natural ...
arXiv:2102.05210v1
fatcat:4q5zfadzfbdhlhkc2mshzwlrwe
Deep Learning in Multi-organ Segmentation
[article]
2020
arXiv
pre-print
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We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge ...
For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. ...
Unlike U-Net, Dense-U-Nets uses asymmetric encoder and decoder. ...
arXiv:2001.10619v1
fatcat:6uwqwnzydzccblh5cajhsgdpea
Modality specific U-Net variants for biomedical image segmentation: A survey
[article]
2022
arXiv
pre-print
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of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various modalities. ...
This article contributes in presenting the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants by performing 1) inter-modality, and ...
Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary ...
arXiv:2107.04537v4
fatcat:m5oqea5q6vhbhkerjmejder3hu
LDNNET: Towards Robust Classification of Lung Nodule and Cancer using Lung Dense Neural Network
2021
IEEE Access
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Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder-decoder architecture. ...
[18] proposed a novel lung nodule detection and classification model using one stage detector called as "I3DR-Net." ...
Author Name: Preparation of Papers for IEEE Access (February 2017) ...
doi:10.1109/access.2021.3068896
fatcat:b65ytjf34bgudotehv3jmfif74
Deep Neural Architectures for Medical Image Semantic Segmentation: Review
2021
IEEE Access
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Hybrid densely connected U-Net abbreviated as H-DenseUNet was proposed in [13] . ...
It is convolution neural network with dense connections. The dense mechanism used in a network maximizes the information and gradient flow. ...
doi:10.1109/access.2021.3086530
fatcat:hacpqwdxybh63j5ygebqszm7qq
Pulmonary nodule detection using 3D Residual U-net oriented context-guided attention and multi-branch classification network
2021
IEEE Access
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The contributions include: (1) Nodule candidate detection: 3D Residual U-Net model is improved to detect candidate nodules, which constructs 3D context-guided module to extract local and global nodule ...
features by setting the dilated convolution with different dilation rates. ...
[6] detected candidate nodules by using multi-scale Laplacian of Gaussian (LoG) filters and densely dilated 3D deep convolutional neural network (DCNN) to classify candidate nodules, but it contained ...
doi:10.1109/access.2021.3137317
fatcat:svjcruozfbdcpoc6is2bbuijli
SA-Net: A scale-attention network for medical image segmentation
2021
PLoS ONE
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The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst ...
In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention ...
We evaluate SA-Net on 2D lung CT images provided by the Lung Nodule Analysis (LUNA) competition, in which two challenges have been made, namely nodule detection and false-positive reduction. ...
doi:10.1371/journal.pone.0247388
pmid:33852577
pmcid:PMC8046243
fatcat:zme55aqkunezzn45yr33ug5x74
U-Net Convolutional Networks Performance Based on Software-Hardware Cooperation Parameters: A Review
2022
International Journal of Computing and Digital Systems
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U-Net, as convolutional neural network (CNN), is one of the deep learning architectures that have been utilized to perform segmentation in several applications. ...
The flexible design of the U-Net, utilizing the data augmentation approach, has been contributed in the achievement of successful predictive results for different image sizes particularly with training ...
[10] proposed new Dense Multi-path U-Net for multimodality segmentation. ...
doi:10.12785/ijcds/110180
fatcat:gua2kp5cknhmxlnnmanu3d43ma
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