Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN
Joint Authors
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-28
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Using deep neural networks for segmenting an MRI image of heterogeneously distributed pixels into a specific class assigning a label to each pixel is the concept of the proposed approach.
This approach facilitates the application of the segmentation process on a preprocessed MRI image, with a trained network to be utilized for other test images.
As labels are considered expensive assets in supervised training, fewer training images and training labels are used to obtain optimal accuracy.
To validate the performance of the proposed approach, an experiment is conducted on other test images (available in the same database) that are not part of the training; the obtained result is of good visual quality in terms of segmentation and quite similar to the ground truth image.
The average computed Dice similarity index for the test images is approximately 0.8, whereas the Jaccard similarity measure is approximately 0.6, which is better compared to other methods.
This implies that the proposed method can be used to obtain reference images almost similar to the segmented ground truth images.
American Psychological Association (APA)
Khagi, Bijen& Kwon, Goo-Rak. 2018. Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187166
Modern Language Association (MLA)
Khagi, Bijen& Kwon, Goo-Rak. Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN. Journal of Healthcare Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1187166
American Medical Association (AMA)
Khagi, Bijen& Kwon, Goo-Rak. Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187166
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187166