Automatic deep learning-based segmentation of neonatal cerebral ventricles from 3D ultrasound images

2021 ◽  
Author(s):  
Zachary Szentimrey ◽  
Sandrine de Ribaupierre ◽  
Aaron Fenster ◽  
Eranga Ukwatta
Keyword(s):  
Deep Learning ◽  
3D Ultrasound ◽  
Ultrasound Images ◽  
Cerebral Ventricles

scholarly journals Deep transfer learning for aortic root dilation identification in 3D ultrasound images

2018 ◽  
Vol 4 (1) ◽  
pp. 71-74 ◽  
Author(s):  
Jannis Hagenah ◽  
Mattias Heinrich ◽  
Floris Ernst
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Aortic Root ◽  
Classification Accuracy ◽  
3D Ultrasound ◽  
Small Data ◽  
Data Sets ◽  
Ultrasound Images ◽  
Small Data Sets ◽  
Learned Features

AbstractPre-operative planning of valve-sparing aortic root reconstruction relies on the automatic discrimination of healthy and pathologically dilated aortic roots. The basis of this classification are features extracted from 3D ultrasound images. In previously published approaches, handcrafted features showed a limited classification accuracy. However, feature learning is insufficient due to the small data sets available for this specific problem. In this work, we propose transfer learning to use deep learning on these small data sets. For this purpose, we used the convolutional layers of the pretrained deep neural network VGG16 as a feature extractor. To simplify the problem, we only took two prominent horizontal slices throgh the aortic root, the coaptation plane and the commissure plane, into account by stitching the features of both images together and training a Random Forest classifier on the resulting feature vectors. We evaluated this method on a data set of 48 images (24 healthy, 24 dilated) using 10-fold cross validation. Using the deep learned features we could reach a classification accuracy of 84 %, which clearly outperformed the handcrafted features (71 % accuracy). Even though the VGG16 network was trained on RGB photos and for different classification tasks, the learned features are still relevant for ultrasound image analysis of aortic root pathology identification. Hence, transfer learning makes deep learning possible even on very small ultrasound data sets.

Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images

2017 ◽  
Vol 35 ◽  
pp. 181-191 ◽  
Author(s):  
Wu Qiu ◽  
Yimin Chen ◽  
Jessica Kishimoto ◽  
Sandrine de Ribaupierre ◽  
Bernard Chiu ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
3D Ultrasound ◽  
Ultrasound Images ◽  
Cerebral Ventricles ◽  
Segmentation Approach

Longitudinal Analysis of Pre-Term Neonatal Cerebral Ventricles From 3D Ultrasound Images Using Spatial-Temporal Deformable Registration

2017 ◽  
Vol 36 (4) ◽  
pp. 1016-1026 ◽  
Author(s):  
Wu Qiu ◽  
Yimin Chen ◽  
Jessica Kishimoto ◽  
Sandrine de Ribaupierre ◽  
Bernard Chiu ◽  
...  
Keyword(s):  
Longitudinal Analysis ◽  
3D Ultrasound ◽  
Deformable Registration ◽  
Ultrasound Images ◽  
Cerebral Ventricles

Automated 3D U‐net based segmentation of neonatal cerebral ventricles from 3D ultrasound images

2021 ◽  
Author(s):  
Szentimrey Zachary ◽  
de Ribaupierre Sandrine ◽  
Fenster Aaron ◽  
Ukwatta Eranga
Keyword(s):  
3D Ultrasound ◽  
Ultrasound Images ◽  
Cerebral Ventricles

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

Cataract detection based on ocular B-ultrasound images by collaborative monitoring deep learning

2021 ◽  
pp. 107442
Author(s):  
Yong Wang ◽  
Chenwei Tang ◽  
Jian Wang ◽  
Yongsheng Sang ◽  
Jiancheng Lv
Keyword(s):  
Deep Learning ◽  
Ultrasound Images
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