scholarly journals OC11.02: A multicentre, multi‐device validation of a deep learning system for the automated segmentation of fetal brain structures from two‐dimensional ultrasound images

2021 ◽  
Vol 58 (S1) ◽  
pp. 33-33
Author(s):  
A. Narayan ◽  
S. Kaushik ◽  
H. Shankar ◽  
S. Jain ◽  
N. Hegde ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fetal Brain ◽  
Brain Structures ◽  
Learning System ◽  
Ultrasound Images ◽  
Automated Segmentation ◽  
Two Dimensional ◽  
Device Validation

Deep learning segmentation of general interventional tools in two‐dimensional ultrasound images

2020 ◽  
Vol 47 (10) ◽  
pp. 4956-4970
Author(s):  
Derek J. Gillies ◽  
Jessica R. Rodgers ◽  
Igor Gyacskov ◽  
Priyanka Roy ◽  
Nirmal Kakani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ultrasound Images ◽  
Two Dimensional

scholarly journals Automated Segmentation of Thyroid Nodule, Gland, and Cystic Components From Ultrasound Images Using Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 63482-63496 ◽  
Author(s):  
Viksit Kumar ◽  
Jeremy Webb ◽  
Adriana Gregory ◽  
Duane D. Meixner ◽  
John M. Knudsen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Ultrasound Images ◽  
Automated Segmentation

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

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

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 Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

scholarly journals A deep-learning system to classify lung X-ray images into normal/pneumonia class

2020 ◽  
Vol 101 ◽  
pp. 209
Author(s):  
R. Baskaran ◽  
B. Ajay Rajasekaran ◽  
V. Rajinikanth
Keyword(s):  
Deep Learning ◽  
Learning System ◽  
X Ray

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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