Paper Info
Title
Hybrid Approach For Automatic Segmentation Of Fetal Abdomen From Ultrasound Images Using Deep Learning
Abstract
In this paper, we propose a hybrid approach combining traditional texture analysis methods with deep learning for the automatic detection and measurement of abdominal contour from 2-D fetal ultrasound images. Following a learning-based procedure for region of interest (ROI) localization to segment the abdominal boundary, we show that convolutional neural networks (CNNs) outperform other state-of-the-art texture features and conventional classifiers, in addressing the binary classification problem of distinguishing between abdomen versus non-abdomen regions. However, we obtain significantly better segmentation results in identifying the best ROI containing fetal abdomen, when the predictions from CNN are combined with those from gradient boosting machine (GBM) using histogram of oriented gradient (HOG) features. We trained our method on a set of 70 images and tested them on another distinct set of 70 images. We obtained a mean DICE similarity coefficient of 0.90, which shows excellent overlap with the ground truth. We report that the mean computed gestational age difference between our segmentation results and the ground truth, is within two weeks for 90% (and within one week for 70%) of the testing cases.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493382
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Field
DocType
ISSN
Histogram,Computer vision,Pattern recognition,Binary classification,Convolutional neural network,Computer science,Segmentation,Ground truth,Artificial intelligence,Deep learning,Region of interest,Gradient boosting
Conference
1945-7928
Citations 
PageRank 
References 
5
0.53
9
Authors
4
Name
Order
Citations
PageRank
Hariharan Ravishankar1192.62
Sahana M. Prabhu291.93
Vivek Vaidya3294.43
Nitin Singhal411310.55