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AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics [article]

Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim
2022 arXiv   pre-print
This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks.  ...  AI-based detection searches the image space to find the regions of interest based on patterns and features.  ...  Acknowledgements This project was in part supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06467, and the Canadian Institutes of Health Research  ... 
arXiv:2110.10332v4 fatcat:vmpxhoolarbrve5ddyfn5umfim

A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis

Xukun Li, Yukun Zhou, Peng Du, Guanjing Lang, Min Xu, Wei Wu
2020 Applied intelligence (Boston)  
Then, four state-of-the-art 3D convolution neural network (CNN) models were trained and evaluated in the inspection of PTB CT images.  ...  AbstractThe purpose of this study was to establish and validate a new deep learning system that generates quantitative computed tomography (CT) reports for the diagnosis of pulmonary tuberculosis (PTB)  ...  V-Net is a 3D version of U-Net with resnet blocks [38] , which has been demonstrated as a effective method on the feature extraction part of lung CT image, in the domain of pulmonary nodule detections  ... 
doi:10.1007/s10489-020-02051-1 fatcat:3sy55mtqwbceljzhpo26vovlre

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
2021 Multimedia tools and applications  
It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA.  ...  This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.  ...  Zhao and Zeng 2019 [190] proposed DLA based on supervised MSS U-Net and 3DU-Net to automatically segment kidneys and kidney tumors from CT images. In the present pandemic situation, Fan et al.  ... 
doi:10.1007/s11042-021-10707-4 pmid:33841033 pmcid:PMC8023554 fatcat:cm522go4nbdbnglgzpw4nu7tbi

Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation [article]

Vemund Fredriksen, Svein Ole M. Svele, André Pedersen, Thomas Langø, Gabriel Kiss, Frank Lindseth
2021 arXiv   pre-print
Methods: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed  ...  Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel.  ...  .: Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.  ... 
arXiv:2112.11541v1 fatcat:kuxnh7e3xndbfkj652nqazc3qy

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
Recent studies propose automatic segmentation methods for segmenting necrotic femoral head based on k-means [1] or deep learning [2] .  ...  A senior radiologist pinpointed the nodules' location on the CT and T1PC sequences, and each lesion was segmented in both modalities using the semi-automatic segmentation algorithm FastGrowCut.  ...  Artificial intelligence coronary calcium scoring in low dose chest CT-Ready to go?  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
of Emphysema in CT Scans: Beyond Density Mask Gonzalo Vegas Sanchez-Ferrero*; Raul San Jose Estepar T-58 Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation  ...  Learning Jianpeng Zhang; Yutong Xie; Qi Wu; Yong Xia* M-109 SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks Md.  ...  T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation [article]

Ali Hatamizadeh
2020 arXiv   pre-print
In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream  ...  Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets.  ...  We introduced a new 3D CNN-based segmentation technique, namely, Progressive Dense V-Networks (PDV-Nets), and applied it to the automatic, fast, and reliable segmentation of lung lobes from chest CT scans  ... 
arXiv:2006.12706v1 fatcat:6jchhrv6zrhlhbpcak6fcbh4a4

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment  ...  Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this  ...  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

Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation [article]

Shuchao Pang, Anan Du, Mehmet A. Orgun, Yan Wang, Quan Z. Sheng, Shoujin Wang, Xiaoshui Huang, Zhenmei Yu
2022 arXiv   pre-print
More importantly, the newly built GER-UNet also shows potential in reducing the sample complexity and the redundancy of filters, upgrading current segmentation CNNs and delineating organs on other medical  ...  Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis.  ...  Winkels, and Cohen also utilized the equivariant CNNs for pulmonary nodule detection in CT scans [15] .  ... 
arXiv:2207.14472v1 fatcat:advrih7varafhohc3glne3hxvm

Clinical Radiology Exhibits

2021 Journal of Medical Imaging and Radiation Oncology  
Results: Fifty-one procedures were performed in 50 patients (34 male, 66.7%). Most patients had tandem ICA and intracranial lesions (n = 34, 66.7%).  ...  Post-procedure DUS was performed in 34 patients (66.7%), 27 (79.4%) of whom demonstrated stent patency.  ...  Isensee F, Jaeger P, Kohl S, Petersen J, Maier-Hein K. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.  ... 
doi:10.1111/1754-9485.13301 fatcat:x3hy5vd2lzarld4p2vvirlvncq

B - Scientific Sessions

2010 Insights into Imaging  
Results: In all patients, the entire pancreas was completely covered by the 16 cm scan field. Automatic image registration failed in two patients and manual adjustments were needed.  ...  After intravenous contrast media injection (8 ml/s, 350 mg iodine/ml) and dynamic density measurements in the right ventricle, dynamic scanning was initiated manually. 15 low-dose 16 cm volume scans were  ...  B-680 10:39 Automatic exposure control in standard CT scans of children and adults: What is the reduction in organ and effective dose? A.E. Papadakis, K. Perisinakis, I. Oikonomou, J.  ... 
doi:10.1007/s13244-010-0011-1 pmid:23099631 pmcid:PMC3534347 fatcat:yfnq3lgteja2lfjl66ivdfnoum

Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging

Qianli Ma, Jielong Yan, Jun Zhang, Qiduo Yu, Yue Zhao, Chaoyang Liang, Donglin Di
2022 Frontiers in Medicine  
To tackle the shortage of high-quality data and improve the sensitivity of diagnosis, we propose a Cost-Sensitive Uncertainty Hypergraph Learning (CSUHL) model to identify the lymph node based on the CT  ...  Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy.  ...  We first apply the deep learning pre-trained method, named VB-Net (31), to segment the left/right lung, 5 lung lobes, 18 lung segments and infection lesions for each CT image in the portal software.  ... 
doi:10.3389/fmed.2022.840319 pmid:35223932 pmcid:PMC8866560 fatcat:3d5kpzrqyfcclb6ev5imdwsm7y

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
All nodules were placed in an anthropomorphic phantom and scanned with a 16-detector row CT scanner.  ...  In this study, we propose a method that combines an articulated statistical shape model and a local exemplar-based appearance model for automatically segmenting hand bones in CT.  ...  2 cm) in tens of minutes yielding images containing millions of spectra. Spectra are then automatically classified as one of seven cell-types in prostate tissue in a matter of seconds.  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

ECR 2011 Book of Abstracts - B - Scientific Sessions

2011 Insights into Imaging  
Results: In 20 HCCs concordant hypervascular profile was shown between CT and MR during the arterial phase, while discordant vascularity profile was identified in 11 nodules appearing isovascular on CT  ...  Methods and Materials: Thirty-one biopsy-proven HCCs (diameter, 1-3 cm) in 18 consecutive cirrhotic patients (12 males and 6 females; age: 50±13 years) scanned by multiphase contrast-enhanced 64-row multidetector  ...  B-274 14:27 Automatic detection and quantification of emphysema in healthy smokers: CT findings in correlation with pulmonary function tests (PFTs) K. Yasunaga, N. Cherot-Kornobis, J.-L. Edme, A.  ... 
doi:10.1007/s13244-011-0077-4 pmid:23100071 pmcid:PMC3533624 fatcat:lytbu2vohbhhnorjqlpogl77iu

A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology

Kjell Erlandsson, Irène Buvat, P Hendrik Pretorius, Benjamin A Thomas, Brian F Hutton
2012 Physics in Medicine and Biology  
Accurate quantification in PET and SPECT requires correction for a number of physical factors, such as photon attenuation, Compton scattering and random coincidences (in PET).  ...  Spatial resolution-related effects, referred to as 'partial volume effects' (PVEs), depend not only on the characteristics of the imaging system but also on the object and activity distribution.  ...  Acknowledgments We want to thank Susan Landau and William Jagust (Jagust Lab, University of California, Berkeley) for kindly providing data for one of the clinical examples.  ... 
doi:10.1088/0031-9155/57/21/r119 pmid:23073343 fatcat:zumt2lualjhrnlirtw2okjzf6e
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