| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Jianlin Chen, Jinfeng Zhang, Jingjing Xiang, Jie Yu, Fanghui Qi. Impact of Intelligent Convolutional Neural Network -based Algorithms on Head Computed Tomography Evaluation and Comprehensive Rehabilitation Acupuncture Therapy for Patients with Cerebral Infarction. Journal of neuroscience methods. 2024-06-08. PMID:38851543. |
this work was to evaluate the impacts of comprehensive rehabilitation acupuncture therapy on the recovery of neurological function in cerebral infarction (ci) patients and to utilize convolutional neural network (cnn) intelligent algorithms to optimize head computed tomography (ct) images and improve lesion localization accuracy. |
2024-06-08 |
2024-06-11 |
Not clear |
| Xiaofan Xiong, Stephen A Graves, Brandie A Gross, John M Buatti, Reinhard R Beiche. Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN. Tomography (Ann Arbor, Mich.). vol 10. issue 5. 2024-05-24. PMID:38787017. |
lumbar and thoracic vertebrae segmentation in ct scans using a 3d multi-object localization and segmentation cnn. |
2024-05-24 |
2024-05-27 |
Not clear |
| Saddam Hussain Khan, Javed Iqbal, Syed Agha Hassnain, Muhammad Owais, Samih M Mostafa, Myriam Hadjouni, Amena Mahmou. COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs. Expert systems with applications. vol 229. 2023-05-23. PMID:37220492. |
in the first phase, a novel sb-stm-brnet cnn is developed, incorporating a new channel squeezed and boosted (sb) and dilated convolutional-based split-transform-merge (stm) block to detect covid-19 infected lung ct images. |
2023-05-23 |
2023-08-14 |
human |
| Yanhang Tong, Bimeng Jie, Xuebing Wang, Zineng Xu, Peng Ding, Yang H. Is Convolutional Neural Network Accurate for Automatic Detection of Zygomatic Fractures on Computed Tomography? Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons. 2023-05-22. PMID:37217163. |
this research is aimed to evaluate the performance of an automatic algorithm for the detection of zygomatic fractures based on convolutional neural network (cnn) on spiral computed tomography (ct). |
2023-05-22 |
2023-08-14 |
Not clear |
| Daisuke Kawahara, Nobuki Imano, Riku Nishioka, Yasushi Nagat. Image masking using convolutional networks improves performance classification of radiation pneumonitis for non-small cell lung cancer. Physical and engineering sciences in medicine. 2023-03-28. PMID:36976438. |
this study proposes a prediction model for rp grade ≥ 2 using a convolutional neural network (cnn) model with image cropping. the 3d computed tomography (ct) images cropped in the whole-body, normal lung (nlung), and nlung regions overlapping the region over 20 gy (nlung∩20 gy) used in treatment planning were used as the input data. |
2023-03-28 |
2023-08-14 |
Not clear |
| Umberto A Gava, Federico D'Agata, Enzo Tartaglione, Riccardo Renzulli, Marco Grangetto, Francesca Bertolino, Ambra Santonocito, Edwin Bennink, Giacomo Vaudano, Andrea Boghi, Mauro Bergu. Neural network-derived perfusion maps: A model-free approach to computed tomography perfusion in patients with acute ischemic stroke. Frontiers in neuroinformatics. vol 17. 2023-03-27. PMID:36970658. |
in this study, we investigate whether a convolutional neural network (cnn) can generate informative parametric maps from the pre-processed ct perfusion data in patients with acute ischemic stroke in a clinical setting. |
2023-03-27 |
2023-08-14 |
Not clear |
| Hulin Kuang, Wenfang Wan, Yahui Wang, Jie Wang, Wu Qi. Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network. Biomedicines. vol 11. issue 2. 2023-02-25. PMID:36830780. |
automated collateral scoring on ct angiography of patients with acute ischemic stroke using hybrid cnn and transformer network. |
2023-02-25 |
2023-08-14 |
Not clear |
| Martina Sollini, Margarita Kirienko, Noemi Gozzi, Alessandro Bruno, Chiara Torrisi, Luca Balzarini, Emanuele Voulaz, Marco Alloisio, Arturo Chit. The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the "Real World"? Cancers. vol 15. issue 2. 2023-01-21. PMID:36672306. |
nevertheless, some drawbacks still characterize the supervised learning paradigm employed in networks such as cnn and retina u-net in real-world clinical scenarios, with ct scans from different devices with different sensors' fingerprints and spatial resolution. |
2023-01-21 |
2023-08-14 |
Not clear |
| Sami Azam, A K M Rakibul Haque Rafid, Sidratul Montaha, Asif Karim, Mirjam Jonkman, Friso De Boe. Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches to Classify Lung Diseases. Biomedicines. vol 11. issue 1. 2023-01-21. PMID:36672641. |
to classify the ct scans into three classes, two deep learning architectures, (a) a convolutional neural network (cnn) and (b) a cnn with long short-term memory (lstm) and an attention mechanism, are considered. |
2023-01-21 |
2023-08-14 |
Not clear |
| Oznur Ozaltin, Orhan Coskun, Ozgur Yeniay, Abdulhamit Subas. A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet. Bioengineering (Basel, Switzerland). vol 9. issue 12. 2022-12-23. PMID:36550989. |
in this paper, we designed hybrid algorithms that include a new convolution neural networks (cnn) architecture called oznet and various machine learning algorithms for binary classification of real brain stroke ct images. |
2022-12-23 |
2023-08-14 |
Not clear |
| Amish Kumar, Palash Ghosal, Soumya Snigdha Kundu, Amritendu Mukherjee, Debashis Nand. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. Computer methods and programs in biomedicine. vol 226. 2022-10-08. PMID:36208537. |
this paper has introduced a patch-based, residual, asymmetric, encoder-decoder cnn that solves two major problems in acute ischemic stroke lesion segmentation from ct and ct perfusion data using deep neural networks. |
2022-10-08 |
2023-08-14 |
Not clear |
| Nelly Abbani, Thomas Baudier, Simon Rit, Francesca di Franco, Franklin Okoli, Vincent Jaouen, Florian Tilquin, Anaïs Barateau, Antoine Simon, Renaud de Crevoisier, Julien Bert, David Sarru. Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam CT. Medical physics. 2022-08-24. PMID:36000762. |
in this work, we propose an alternative approach: to train a cnn (using a deep learning-based segmentation tool called nnu-net) from a database of artificial cbct images simulated from planning ct, for which it is easier to obtain the organ contours. |
2022-08-24 |
2023-08-14 |
Not clear |
| Xiaoxia Chen, Xiao Bai, Xin Shu, Xucheng He, Jinjing Zhao, Xiaodong Guo, Guisheng Wan. Optimized Deconvolutional Algorithm-based CT Perfusion Imaging in Diagnosis of Acute Cerebral Infarction. Contrast media & molecular imaging. vol 2022. 2022-07-08. PMID:35800236. |
to apply deconvolution algorithm in computer tomography (ct) perfusion imaging of acute cerebral infarction (aci), a convolutional neural network (cnn) algorithm was optimized first. |
2022-07-08 |
2023-08-14 |
Not clear |
| Bo Yang, Yankui Chang, Yongguang Liang, Zhiqun Wang, Xi Pei, Xie George Xu, Jie Qi. A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality. Frontiers in oncology. vol 12. 2022-06-16. PMID:35707352. |
the aim of this study is to compare two methods for improving the image quality of the varian halcyon cone-beam ct (icbct) system through the deformed planning ct (dpct) based on the convolutional neural network (cnn) and the synthetic ct (sct) generation based on the cycle-consistent generative adversarial network (cyclegan). |
2022-06-16 |
2023-08-14 |
Not clear |
| Nico Sollmann, Maximilian T Löffler, Malek El Husseini, Anjany Sekuboyina, Michael Dieckmeyer, Sebastian Rühling, Claus Zimmer, Bjoern Menze, Gabby B Joseph, Thomas Baum, Jan S Kirschk. Automated opportunistic osteoporosis screening in routine computed tomography of the spine - comparison with dedicated quantitative CT. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research. 2022-05-22. PMID:35598311. |
the purpose of this study was to compare lumbar volumetric bone mineral density (vbmd) assessed by a convolutional neural network (cnn)-based framework in routine ct to vbmd from dedicated quantitative ct (qct), and to evaluate the ability of vbmd and surrogate measurements of hounsfield units (hu) to distinguish between patients with and without osteoporotic vertebral fractures (vfs). |
2022-05-22 |
2023-08-13 |
Not clear |
| Edward G A Henderson, Eliana M Vasquez Osorio, Marcel van Herk, Andrew F Gree. Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data. Physics and imaging in radiation oncology. vol 22. 2022-05-06. PMID:35514528. |
the aim of this study was to develop a cnn capable of accurate head and neck (hn) 3d auto-segmentation of planning ct scans using a small training dataset (34 cts). |
2022-05-06 |
2023-08-13 |
Not clear |
| Yung-Ting Chen, Yao-Liang Chen, Yi-Yun Chen, Yu-Ting Huang, Ho-Fai Wong, Jiun-Lin Yan, Jiun-Jie Wan. Deep Learning-Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke. Diagnostics (Basel, Switzerland). vol 12. issue 4. 2022-04-23. PMID:35453855. |
this study proposed the use of convolutional neural network (cnn)-based deep learning models for efficient classification of strokes based on unenhanced brain ct image findings into normal, hemorrhage, infarction, and other categories. |
2022-04-23 |
2023-08-13 |
Not clear |
| P Buelens, S Willems Ir, L Vandewinckele I, W Crijns, F Maes Ir, C G Welten. Clinical Evaluation of a Deep Learning Model for Segmentation of Target Volumes in Breast Cancer Radiotherapy. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. 2022-04-21. PMID:35447286. |
the aim of this study is to evaluate the performance and efficiency of segmentation of ctvs in planning ct images of breast cancer patients using a 3d convolutional neural network (cnn) compared to the manual process. |
2022-04-21 |
2023-08-13 |
Not clear |
| Simone Saitta, Francesco Sturla, Alessandro Caimi, Alessandra Riva, Maria Chiara Palumbo, Giovanni Nano, Emiliano Votta, Alessandro Della Corte, Mattia Glauber, Dante Chiappino, Massimiliano M Marrocco-Trischitta, Alberto Redaell. A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography. Journal of digital imaging. 2022-01-27. PMID:35083618. |
we trained and applied a fully automated pipeline embedding a convolutional neural network (cnn), which feeds on 3d ct images to automatically segment the thoracic aorta, detects proximal landing zones (plzs), and quantifies geometric features that are relevant for tevar planning. |
2022-01-27 |
2023-08-13 |
Not clear |
| Simone Saitta, Francesco Sturla, Alessandro Caimi, Alessandra Riva, Maria Chiara Palumbo, Giovanni Nano, Emiliano Votta, Alessandro Della Corte, Mattia Glauber, Dante Chiappino, Massimiliano M Marrocco-Trischitta, Alberto Redaell. A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography. Journal of digital imaging. 2022-01-27. PMID:35083618. |
for 465 ct scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a cnn with a 3d u-net architecture. |
2022-01-27 |
2023-08-13 |
Not clear |