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

Citation Author(s):
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan
Submitted by:
yasmin kassim
Last updated:
18 September 2019 - 10:11pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Dr. kannappan palaniappan
Paper Code:
1706

Abstract 

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.

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Dataset Files

RF_OFB_UNet.pdf

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