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3D convolutional neural network for automatic detection of lung nodules in chest CT

Sardar Hamidian, Berkman Sahiner, Nicholas Petrick, Aria Pezeshk, Nicholas A. Petrick, Samuel G. Armato
2017 Medical Imaging 2017: Computer-Aided Diagnosis  
In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset.  ...  Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images.  ...  Deep learning approaches to lung nodule detection in chest CT have so far relied on 2D views of nodules and CT slices. 9 In this paper we report a computer-aided detection (CADe) system based on 3D CNNs  ... 
doi:10.1117/12.2255795 pmid:28845077 pmcid:PMC5568782 dblp:conf/micad/HamidianSPP17 fatcat:jeqnjcvanjaodjr7ammori3shq

Deep Learning for Automated Medical Image Analysis [article]

Wentao Zhu
2019 arXiv   pre-print
Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images.  ...  Lastly, we will demonstrate the AnatomyNet which is thousands of times faster and more accurate than previous methods on automated anatomy segmentation.  ...  Traditional anatomical segmentation methods use primarily atlas-based methods, producing segmentations by aligning new images to a fixed set of manually labelled exemplars [74] .  ... 
arXiv:1903.04711v1 fatcat:xigyugddlrentc42o5mnlbhdkq

A Pulmonary Nodule Detection Model Based on Progressive Resolution and Hierarchical Saliency [article]

Junjie Zhang, Yong Xia, Yanning Zhang
2018 arXiv   pre-print
Detection of pulmonary nodules on chest CT is an essential step in the early diagnosis of lung cancer, which is critical for best patient care.  ...  Specifically, we design a 3D progressive resolution-based densely dilated FCN, namely the progressive resolution network (PRN), to detect nodule candidates inside the lung, and construct a densely dilated  ...  a score of 0.958 on the LUNA16 dataset, which is higher than the current best challenge record and, to our knowledge, is so far the best performance ever achieved on this dataset.  ... 
arXiv:1807.00598v1 fatcat:ar5yp4ixwncb5nchihf537poem

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

Jianning Chi, Shuang Zhang, Xiaosheng Yu, Chengdong Wu, Yang Jiang
2020 Sensors  
We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection.  ...  Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer.  ...  (a-i) are: ground truth nodule in the given chest CT image, nodule detected by 3D-FCN, MR-CNN, 3D-UNET, PRN-HSN, DCNN, CLAHE-SVM, MASK-RCNN and our proposed method.  ... 
doi:10.3390/s20154301 pmid:32752225 pmcid:PMC7435753 fatcat:6yc2y5zxqrfslo2aylpomztma4

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
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  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  ...  Disclosures The authors declare no conflicts of interest.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance [chapter]

Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados
2017 Lecture Notes in Computer Science  
We evaluate the CASED learning framework on the task of lung nodule detection in chest CT.  ...  We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance.  ...  Diagnosis of this pathology is informed by the presence of malignant pulmonary nodules that appear in thoracic computed tomography (CT) images [6] .  ... 
doi:10.1007/978-3-319-66179-7_73 fatcat:5z5igwron5g3dcwov76lpa3coa

A Novel Deep Learning Network and Its Application for Pulmonary Nodule Segmentation

Dechuan Lu, Junfeng Chu, Rongrong Zhao, Yuanpeng Zhang, Guangyu Tian, Shengrong Gong
2022 Computational Intelligence and Neuroscience  
In this study, we propose a novel network for pulmonary nodule segmentation from CT images based on U-NET.  ...  Pulmonary nodules are the early manifestation of lung cancer, which appear as circular shadow of no more than 3 cm on the computed tomography (CT) image.  ...  Acknowledgments is work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant (BK20201441).  ... 
doi:10.1155/2022/7124902 fatcat:llemwinvv5hotiosey4nonxu2i

Two-stage multitask U-Net construction for pulmonary nodule segmentation and malignancy risk prediction

Yangfan Ni, Zhe Xie, Dezhong Zheng, Yuanyuan Yang, Weidong Wang
2021 Quantitative Imaging in Medicine and Surgery  
Accurate segmentation of pulmonary nodules is important for image-driven nodule analysis and nodule malignancy risk prediction.  ...  Experimental results showed that our method achieved state-of-art results on the Lung Image Database Consortium and Image Database Resource Initiative dataset.  ...  The implementations of 3D FCN and 3D U-Net were borrowed from the semantic segmentation methods code base (39). The V-Net is provided by the code base (40).  ... 
doi:10.21037/qims-21-19 pmid:34993079 pmcid:PMC8666775 fatcat:abwrbqxa3zea5dddd3yiasxhui

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  
Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant.  ...  Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification.  ...  Segmentation Based on Lung Nodule Type As mentioned above, pulmonary nodules have different types, shapes, and clinical features.  ... 
doi:10.3390/diagnostics12020298 pmid:35204388 pmcid:PMC8871398 fatcat:zbasqznr5vblnkfmeuzwlmqbom

A review of the application of deep learning in medical image classification and segmentation

Lei Cai, Jingyang Gao, Di Zhao
2020 Annals of Translational Medicine  
of medical images and the work of our team in the field of big data analysis of medical imagec, especially the classification and segmentation of medical images.  ...  This review introduces the application of intelligent imaging and deep learning in the field of big data analysis and early diagnosis of diseases, combining the latest research progress of big data analysis  ...  Figure 4 shows the enhanced image of breast. Pulmonary nodule screening Pulmonary nodule disease is a common lung disease. Figure 5 shows the whole picture of pulmonary nodule in CT image.  ... 
doi:10.21037/atm.2020.02.44 pmid:32617333 pmcid:PMC7327346 fatcat:bywo4riijzemnlu6nilxzdumwu

Nodule-plus R-CNN and Deep Self-paced Active Learning for 3D Instance Segmentation of Pulmonary Nodules

Wenzhe Wang, Ruiwei Feng, Jintai Chen, Yifei Lu, Tingting Chen, Hongyun Yu, Danny Z. Chen, Jian Wu
2019 IEEE Access  
INDEX TERMS Pulmonary nodule segmentation, 3D CT images, R-CNN, active learning, self-paced learning.  ...  In this paper, a new region-based network, called Nodule-plus Region-based CNN, is proposed to detect pulmonary nodules in 3D thoracic CT images effectively while synchronously generating an instance segmentation  ...  Based on recent 3D image segmentation methods [15] , [16] and 2D image instance segmentation methods like Mask R-CNN [17] , our 3D proposed deep region-based CNN introduces an effective way for pulmonary  ... 
doi:10.1109/access.2019.2939850 fatcat:z37qnsbnizcr7pn7u62ti75t6m

ResNet based Lung Nodules Detection from Computed Tomography Images

Hence, a lung nodule detection method using ResNet in CT images is proposed. The proposed method consists of two stages, the pre-processing stage and nodule detection stage.  ...  LIDC (Lung Image Database Consortium) dataset which contains 1010 CT patients images of chest regions are taken for experimentation.  ...  A simple method based on morphological operations was adopted for segmentation which saves both computation time and cost. The nodule detection stage is based on ResNet with fewer residual blocks.  ... 
doi:10.35940/ijitee.d1540.029420 fatcat:rd2kcgnb2vevflewsbuegw73me

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
Accurate and automatic segmentation of these pulmonary lobes from computed tomography (CT) images is of clinical importance for lung disease assessment and treatment planning.  ...  We refer to the network as Pulmonary Lobe Segmentation Network (PLS-Net), which is designed to efficiently exploit 3D spatial and contextual information from high-resolution volumetric CT images for effective  ...  However, existing 3D FCN-based pulmonary lobe segmentation methods only make use of short-range local contexts due to their small-sized receptive fields.  ... 
arXiv:1909.07474v1 fatcat:awf6lcrktrh5dj4hkpgkmoj5zy

Automatic segmentation of pulmonary lobes on low-dose computed tomography using deep learning

Zewei Zhang, Jialiang Ren, Xiuli Tao, Wei Tang, Shijun Zhao, Lina Zhou, Yao Huang, Jianwei Wang, Ning Wu
2021 Annals of Translational Medicine  
To develop and validate a fully automated deep learning-based segmentation algorithm to segment pulmonary lobe on low-dose computed tomography (LDCT) images.  ...  The segmentation model based on LDCT can automatically and robustly and efficiently segment pulmonary lobes.  ...  Segmentation of pulmonary lobes from LDCT images is a crucially important step for CAD to identify pulmonary nodules easier.  ... 
doi:10.21037/atm-20-5060 pmid:33708918 pmcid:PMC7944332 fatcat:zkjy6byudjeltm5ldyi3i42ire

A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques

Jianrong Wu, Tianyi Qian
2019 Journal of Medical Artificial Intelligence  
Acknowledgements It is so appreciated for support and help from workmates in the project of Miying in Tencent. Footnote Conflicts of Interest: J Wu and T Qian are Tencent employees.  ...  Considerable research efforts have been devoted to pulmonary nodule detection on chest CT images, so as to segmentation and classification.  ...  However, screening CT images manually is a very time-consuming job for radiologists, for there are hundreds of slices in one scan Review Article A survey of pulmonary nodule detection, segmentation and  ... 
doi:10.21037/jmai.2019.04.01 fatcat:s44bw5iwpjf6bpaxqpwi44ysmu
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