Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION

Abstract: 

Automated curvilinear image segmentation is a crucial step to characterize and quantify the morphology of blood vessels across scale. We propose a dual pipeline RF_OFB+U-NET that fuses U-Net deep learning features with a low level image feature filter bank using the random forests classifier for vessel segmentation. We modify the U-Net CNN architecture to provide a foreground vessel regression likelihood map that is used to segment both arteriole and venule blood vessels in mice dura mater tissues. The hybrid approach combining both hand-crafted and learned features was tested on 60 epifluores-cence microscopy images and improved the segmentation of thin vessel structures by nearly 5% using the Dice similarity coefficient compared to U-Net.

up
0 users have voted:

Paper Details

Authors:
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan
Submitted On:
18 September 2019 - 10:11pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Dr. kannappan palaniappan
Paper Code:
1706
Document Year:
2019
Cite

Document Files

RF_OFB_UNet.pdf

(78)

Subscribe

[1] Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan, "DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4696. Accessed: Aug. 10, 2020.
@article{4696-19,
url = {http://sigport.org/4696},
author = {Yasmin M. Kassim; O. V. Glinskii; V. V. Glinsky; V. H. Huxley; G. Guidoboni; K. Palaniappan },
publisher = {IEEE SigPort},
title = {DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION},
year = {2019} }
TY - EJOUR
T1 - DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION
AU - Yasmin M. Kassim; O. V. Glinskii; V. V. Glinsky; V. H. Huxley; G. Guidoboni; K. Palaniappan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4696
ER -
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan. (2019). DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION. IEEE SigPort. http://sigport.org/4696
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan, 2019. DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION. Available at: http://sigport.org/4696.
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan. (2019). "DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION." Web.
1. Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan. DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4696