Deep Learning–based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations

Radiographics ◽  
2021 ◽  
Author(s):  
Yasunori Nagayama ◽  
Daisuke Sakabe ◽  
Makoto Goto ◽  
Takafumi Emoto ◽  
Seitaro Oda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pediatric Ct ◽  
Image Characteristics ◽  
Technical Principles

scholarly journals Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning

2016 ◽  
Author(s):  
Tanel Pärnamaa ◽  
Leopold Parts
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Compartment ◽  
Cell Localization ◽  
Image Characteristics ◽  
Basic Image ◽  
Training Examples

High throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high throughput microscopy.

scholarly journals Automated Segmentation of Abdominal Skeletal Muscle in Pediatric CT Scans Using Deep Learning

2021 ◽  
pp. e200130
Author(s):  
James Castiglione ◽  
Elanchezhian Somasundaram ◽  
Leah A. Gilligan ◽  
Andrew T. Trout ◽  
Samuel Brady
Keyword(s):  
Skeletal Muscle ◽  
Deep Learning ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Pediatric Ct

scholarly journals Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lifang Sun ◽  
Xi Hu ◽  
Yutao Liu ◽  
Hengyu Cai
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Malignant Tumors ◽  
Image Features ◽  
Control Group ◽  
Deep Learning Algorithm ◽  
Image Characteristics ◽  
Traditional Algorithm ◽  
Mri Image

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.

Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction

Radiology ◽  
2021 ◽  
Vol 298 (1) ◽  
pp. 180-188
Author(s):  
Samuel L. Brady ◽  
Andrew T. Trout ◽  
Elanchezhian Somasundaram ◽  
Christopher G. Anton ◽  
Yinan Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Radiation Dose ◽  
Pediatric Ct

scholarly journals Generalizability of deep learning models for dental image analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joachim Krois ◽  
Anselmo Garcia Cantu ◽  
Akhilanand Chaurasia ◽  
Ranjitkumar Patil ◽  
Prabhat Kumar Chaudhari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Root Canal ◽  
Model Performance ◽  
Image Data ◽  
Dental Status ◽  
Learning Models ◽  
Panoramic Radiographs ◽  
Image Characteristics ◽  
Pixel Value ◽  
The Impact

AbstractWe assessed the generalizability of deep learning models and how to improve it. Our exemplary use-case was the detection of apical lesions on panoramic radiographs. We employed two datasets of panoramic radiographs from two centers, one in Germany (Charité, Berlin, n = 650) and one in India (KGMU, Lucknow, n = 650): First, U-Net type models were trained on images from Charité (n = 500) and assessed on test sets from Charité and KGMU (each n = 150). Second, the relevance of image characteristics was explored using pixel-value transformations, aligning the image characteristics in the datasets. Third, cross-center training effects on generalizability were evaluated by stepwise replacing Charite with KGMU images. Last, we assessed the impact of the dental status (presence of root-canal fillings or restorations). Models trained only on Charité images showed a (mean ± SD) F1-score of 54.1 ± 0.8% on Charité and 32.7 ± 0.8% on KGMU data (p < 0.001/t-test). Alignment of image data characteristics between the centers did not improve generalizability. However, by gradually increasing the fraction of KGMU images in the training set (from 0 to 100%) the F1-score on KGMU images improved (46.1 ± 0.9%) at a moderate decrease on Charité images (50.9 ± 0.9%, p < 0.01). Model performance was good on KGMU images showing root-canal fillings and/or restorations, but much lower on KGMU images without root-canal fillings and/or restorations. Our deep learning models were not generalizable across centers. Cross-center training improved generalizability. Noteworthy, the dental status, but not image characteristics were relevant. Understanding the reasons behind limits in generalizability helps to mitigate generalizability problems.

scholarly journals O02 THE ROLE OF DEEP LEARNING IN PREDICTING COMPLEXITY AND COMPLICATIONS IN ABDOMINAL WALL RECONSTRUCTION

2021 ◽  
Vol 108 (Supplement_8) ◽  
Author(s):  
Sharbel Elhage ◽  
Sullivan Ayuso ◽  
Yizi Zhang ◽  
Eva Deerenberg ◽  
Vedra Augenstein ◽  
...  
Keyword(s):  
Deep Learning ◽  
Abdominal Wall Reconstruction ◽  
Management Tool ◽  
Ct Scans ◽  
Pulmonary Failure ◽  
Image Characteristics ◽  
Tertiary Center ◽  
Surgical Complexity ◽  
Operative Planning ◽  
Validation Set

Abstract Aim The aim of our study was to evaluate the utility of image-based deep learning models (DLMs) to predict surgical complexity and postoperative outcomes in patients undergoing AWR. Material and Methods A prospective, tertiary center, hernia database was queried for open AWR patients with adequate pre-operative CT-scans. An 8-layer convolutional neural network (CNN) analyzed image characteristics in Python utilizing the open source Tensorflow© and OpenCV frameworks. Images were analyzed and batched into a training set (80%) and validation set (20%) used to analyze the model output, which was blinded to the CNN until testing. DLMs were run to assess surgical complexity based on need for component separation, surgical site infection (SSI), and pulmonary failure. The surgical complexity DLM was validated by comparison to 6 expert AWR surgeons. Results In total, 369 patient CT scans were utilized. The surgical complexity DLM performed well (ROC=0.744;p&lt;0.0001), and when compared to surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared to 65.0% (p &lt; 0.0001). The SSI DLM was successful with an ROC of 0.898 (p &lt; 0.0001). The DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (p = 0.03). Conclusions DLMs were able to successfully predict surgical complexity and were more accurate than expert surgeons using objective, pre-operative imaging. DLMs were also successful in predicting SSI. This breakthrough may allow for enhanced pre-operative planning, including resource utilization and possible need for tertiary center referral. AI appears to be an exciting new management tool in complex AWR.

Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics

2020 ◽  
Vol 27 (1) ◽  
pp. 82-87 ◽  
Author(s):  
Toru Higaki ◽  
Yuko Nakamura ◽  
Jian Zhou ◽  
Zhou Yu ◽  
Takuya Nemoto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Phantom Study ◽  
Image Characteristics

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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