Using deep‐learning algorithms to classify fetal brain ultrasound images as normal or abnormal

2020 ◽  
Vol 56 (4) ◽  
pp. 579-587 ◽  
Author(s):  
H. N. Xie ◽  
N. Wang ◽  
M. He ◽  
L. H. Zhang ◽  
H. M. Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Fetal Brain ◽  
Ultrasound Images ◽  
Brain Ultrasound

Abstract P379: Automatic Segmentation of Atherosclerotic Plaques in Transverse Carotid Ultrasound Images Using Deep Learning

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Leonard L Yeo ◽  
Melih Engin ◽  
Robin Lange ◽  
David Tang ◽  
Andras Nemes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carotid Artery ◽  
Vessel Wall ◽  
Learning Algorithms ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ultrasound Images ◽  
Carotid Ultrasound ◽  
Accurate Analysis ◽  
Automated Method

Purpose: Ultrasound imaging is commonly used for patients with atheroscelerotic plaques in the carotid artery. While B-mode ultrasound can be used for detection and measurement of these plaques, interpreting these images can be a subjective and time-consuming task. Deep learning algorithms have been proven to be an effective tool for interpreting medical images, especially for classification and segmentation tasks. Here, we propose a deep learning model to automatically detect and measure plaques in transverse B-mode images of the carotid artery. Methods: The proposed automated method takes a transverse B-mode image of the carotid artery as an input and segments the vessel wall in the transverse cross section image using convolutional neural networks. To ensure that the method can perform well in clinical settings, the method has been evaluated on not only healthy subjects (max IMT below 1.2 mm) but also on patients with atheroscelerotic plaques and other vessel wall pathology. Given the B-mode transverse image as an input, the neural network first finds a region of interest (ROI) surrounding the artery and then segments both the inner and outer wall of the carotid artery. We determined the accuracy of the system by F1 Score, a common metric to evaluate the performance of machine learning algorithms. Results: The network was trained and tested on a transverse ultrasound carotid artery dataset that has 506 images, gathered from 4 hospitals. Annotations of an expert reader were used as the ground truth and the performance of the method was evaluated using 5-fold cross validation. The proposed method reaches an F1 score of 0.91 for correctly detecting the ROI and an F1 score of 0.78 for detecting and segmenting the vessel walls in transverse B-mode images. Conclusions: The results show that the proposed deep learning method can be used for accurate analysis and interpretation of carotid ultrasound scans in a clinical setting and potentially reduce the reporting time while increasing objectivity of the analysis.

scholarly journals Deep Learning-Based Methodology for Recognition of Fetal Brain Standard Scan Planes in 2D Ultrasound Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44443-44451 ◽  
Author(s):  
Ruowei Qu ◽  
Guizhi Xu ◽  
Chunxia Ding ◽  
Wenyan Jia ◽  
Mingui Sun
Keyword(s):  
Deep Learning ◽  
Fetal Brain ◽  
Ultrasound Images ◽  
2D Ultrasound

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.

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