scholarly journals OC11.07: Automated grading of fetal cortical development using deep learning algorithms: a preliminary three‐dimensional ultrasound study in healthy fetuses

2021 ◽  
Vol 58 (S1) ◽  
pp. 35-35
Author(s):  
M.K. Wyburd ◽  
L. Hesse ◽  
M. Aliasi ◽  
F. Moser ◽  
M.C. Haak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Cortical Development ◽  
Three Dimensional ◽  
Ultrasound Study ◽  
Automated Grading

scholarly journals Grade and symmetry of normal fetal cortical development: a longitudinal two- and three-dimensional ultrasound study

2010 ◽  
Vol 36 (6) ◽  
pp. 700-708 ◽  
Author(s):  
L. R. Pistorius ◽  
P. Stoutenbeek ◽  
F. Groenendaal ◽  
L. de Vries ◽  
G. Manten ◽  
...  
Keyword(s):  
Cortical Development ◽  
Three Dimensional ◽  
Ultrasound Study

scholarly journals Three-dimensional periodontal investigations using a prototype handheld ultrasound scanner with spatial positioning reading sensor

2021 ◽  
Author(s):  
Radu Chifor ◽  
Mengxun Li ◽  
Kim-Cuong T Nguyen ◽  
Tudor Arsenescu ◽  
Ioana Chifor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Processing ◽  
Soft Tissues ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Movement Analysis ◽  
3D Ultrasound ◽  
Clinical Environment ◽  
Diagnostic Potential ◽  
Spatial Positioning

Aim: To demonstrate the feasibility of the 3D ultrasound periodontal tissue reconstruction of the lateral area of a porcine mandible using standard 2D ultrasound equipment and spatial positioning reading sensors. Material and method: Periodontal 3D reconstructions were performed using a free-hand prototype based on a 2D US scanner and a spatial positioning reading sensor. For automated data processing, deep learning algorithms were implemented and trained using semi-automatically seg-mented images by highly specialized imaging professionals. Results: US probe movement analysis showed that non-parallel 2D frames were acquired during the scanning procedure. Comparing 3 different 3D periodontal reconstructions of the same porcine mandible, the accuracy ranged between 0.179 mm and 0.235 mm. Conclusion: The present study demonstrated the diagnostic potential of 3D reconstruction using a free-hand 2D US scanner with spatial positioning readings. The use of auto-mated data processing with deep learning algorithms makes the process practical in the clinical environment for assessment of periodontal soft tissues.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images

2019 ◽  
Vol 46 (7) ◽  
pp. 3180-3193 ◽  
Author(s):  
Ran Zhou ◽  
Aaron Fenster ◽  
Yujiao Xia ◽  
J. David Spence ◽  
Mingyue Ding
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Ultrasound Images

scholarly journals Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran

2021 ◽  
Vol 35 ◽  
pp. 100825
Author(s):  
Mahdi Panahi ◽  
Khabat Khosravi ◽  
Sajjad Ahmad ◽  
Somayeh Panahi ◽  
Salim Heddam ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Infiltration Rate ◽  
Western Iran ◽  
Rate Prediction ◽  
Cumulative Infiltration

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

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