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An Efficient DA-Net Architecture for Lung Nodule Segmentation

Muazzam Maqsood, Sadaf Yasmin, Irfan Mehmood, Maryam Bukhari, Mucheol Kim
2021 Mathematics  
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

Jianning Chi, Shuang Zhang, Xiaosheng Yu, Chengdong Wu, Yang Jiang
2020 Sensors  
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

Yeganeh Jalali, Mansoor Fateh, Mohsen Rezvani, Vahid Abolghasemi, Mohammad Hossein Anisi
2021 Sensors  
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

Sunyi Zheng, Ludo J Cornelissen, Xiaonan Cui, Xueping Jing, Raymond N J Veldhuis, Matthijs Oudkerk, Peter M A van Ooijen
2020 Medical Physics (Lancaster)  
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

Rui Li, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, Bingding Huang
2022 Diagnostics  
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

Wenming Cao, Rui Wu, Guitao Cao, Zhihai He
2020 IEEE Access  
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]

Hoileong Lee, Tahreema Matin, Fergus Gleeson, Vicente Grau
2019 arXiv   pre-print
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]

Xiangyu Zhao, Peng Zhang, Fan Song, Guangda Fan, Yangyang Sun, Yujia Wang, Zheyuan Tian, Luqi Zhang, Guanglei Zhang
2021 arXiv   pre-print
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]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
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]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
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

Ying Chen, Yerong Wang, Fei Hu, Longfeng Feng, Taohui Zhou, Cheng Zheng
2021 IEEE Access  
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

Muhammad Zubair Khan, Mohan Kumar Gajendran, Yugyung Lee, Muazzam A. Khan
2021 IEEE Access  
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

Haiying Yuan, Yanrui Wu, Junpeng Cheng, Zhongwei Fan, Zhiyong Zeng
2021 IEEE Access  
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

Jingfei Hu, Hua Wang, Jie Wang, Yunqi Wang, Fang He, Jicong Zhang
2021 PLoS ONE  
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

Ula Tarik Salim, Fakhrulddin Ali, Shefa Abdulrahman Dawwd
2022 International Journal of Computing and Digital Systems  
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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