New Fully Automatic Fast Registration Method for 2D Computed Tomography Images

2007 ◽  
Vol 6 (5) ◽  
pp. 761-765 ◽  
Author(s):  
C.G. Ravichandr ◽  
G. Ravindran
Keyword(s):  
Computed Tomography ◽  
Registration Method ◽  
Computed Tomography Images ◽  
Fully Automatic

scholarly journals A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

2020 ◽  
Vol 56 (2) ◽  
pp. 2000775 ◽  
Author(s):  
Shuo Wang ◽  
Yunfei Zha ◽  
Weimin Li ◽  
Qingxia Wu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Risk ◽  
Learning System ◽  
Medical Resource ◽  
Early Prevention ◽  
Prognostic Analysis ◽  
Computed Tomography Images ◽  
Risk Patients ◽  
Fully Automatic

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

scholarly journals Vessel segmentation for automatic registration of untracked laparoscopic ultrasound to CT of the liver

2021 ◽  
Author(s):  
Nina Montaña-Brown ◽  
João Ramalhinho ◽  
Moustafa Allam ◽  
Brian Davidson ◽  
Yipeng Hu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Liver Resection ◽  
Image Guidance ◽  
Laparoscopic Ultrasound ◽  
Multiple Models ◽  
Automatic Registration ◽  
Registration Method ◽  
Fully Automatic ◽  
Tracking Device

Abstract Purpose: Registration of Laparoscopic Ultrasound (LUS) to a pre-operative scan such as Computed Tomography (CT) using blood vessel information has been proposed as a method to enable image-guidance for laparoscopic liver resection. Currently, there are solutions for this problem that can potentially enable clinical translation by bypassing the need for a manual initialisation and tracking information. However, no reliable framework for the segmentation of vessels in 2D untracked LUS images has been presented. Methods: We propose the use of 2D UNet for the segmentation of liver vessels in 2D LUS images. We integrate these results in a previously developed registration method, and show the feasibility of a fully automatic initialisation to the LUS to CT registration problem without a tracking device. Results: We validate our segmentation using LUS data from 6 patients. We test multiple models by placing patient datasets into different combinations of training, testing and hold-out, and obtain mean Dice scores ranging from 0.543 to 0.706. Using these segmentations, we obtain registration accuracies between 6.3 and 16.6 mm in 50% of cases. Conclusions: We demonstrate the first instance of deep learning (DL) for the segmentation of liver vessels in LUS. Our results show the feasibility of UNet in detecting multiple vessel instances in 2D LUS images, and potentially automating a LUS to CT registration pipeline.

scholarly journals A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis

2020 ◽  
Author(s):  
Shuo Wang ◽  
Yunfei Zha ◽  
Weimin Li ◽  
Qingxia Wu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Risk ◽  
Learning System ◽  
High Risk Patients ◽  
Prognostic Analysis ◽  
Computed Tomography Images ◽  
Risk Patients ◽  
Fully Automatic ◽  
Worse Prognosis

AbstractCoronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimization is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images and gene information were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms.Take-home messageFully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis.

An Optimization of System for Automatic Recognition of Ischemic Stroke Areas in Computed Tomography Images

Informatica ◽  
2007 ◽  
Vol 18 (4) ◽  
pp. 603-614
Author(s):  
Darius Grigaitis ◽  
Vaida Bartkutė ◽  
Leonidas Sakalauskas
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Automatic Recognition ◽  
Computed Tomography Images

scholarly journals Free-form tumor synthesis in computed tomography images via richer generative adversarial network

2021 ◽  
Vol 218 ◽  
pp. 106753
Author(s):  
Qiangguo Jin ◽  
Hui Cui ◽  
Changming Sun ◽  
Zhaopeng Meng ◽  
Ran Su
Keyword(s):  
Computed Tomography ◽  
Free Form ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Computed Tomography Images
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