TY - GEN
T1 - Segmentation of Nuclei in Histopathology images using Fully Convolutional Deep Neural Architecture
AU - Yasin Noor Mohamed, Mohamed
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020
Y1 - 2020
N2 - Nuclei segmentation is an initial step in the automated analysis of digitized microscopic images. This paper focuses on utilizing the LinkNET-34 architecture for semantic segmentation of nuclei from the HE stained breast cancer histopathology images. The segmentation process is implemented in two stages where in the first stage the HE stained images are pre-processed to reduce the variance caused because of staining the microscopic images and scanning the slides. During the second stage the preprocessed images are given as input to the LinkNET network which consists of both down-sampling and up-sampling layers. The network is trained using a set of WSI patches released during the Data Science bowl 2018 competition. The performance of the deep learning model is evaluated based on the segmentation accuracy measured using the Dice Coefficient.
AB - Nuclei segmentation is an initial step in the automated analysis of digitized microscopic images. This paper focuses on utilizing the LinkNET-34 architecture for semantic segmentation of nuclei from the HE stained breast cancer histopathology images. The segmentation process is implemented in two stages where in the first stage the HE stained images are pre-processed to reduce the variance caused because of staining the microscopic images and scanning the slides. During the second stage the preprocessed images are given as input to the LinkNET network which consists of both down-sampling and up-sampling layers. The network is trained using a set of WSI patches released during the Data Science bowl 2018 competition. The performance of the deep learning model is evaluated based on the segmentation accuracy measured using the Dice Coefficient.
KW - Dice Co-efficient
KW - LinkNet-34
KW - WSI patches
KW - breast cancer
KW - histopathology images
KW - nuclei segmentation
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U2 - 10.1109/ICCIT-144147971.2020.9213817
DO - 10.1109/ICCIT-144147971.2020.9213817
M3 - Conference contribution
T3 - 2020 International Conference on Computing and Information Technology, ICCIT 2020
BT - 2020 International Conference on Computing and Information Technology (ICCIT-1441)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computing and Information Technology, ICCIT 2020
Y2 - 9 September 2020 through 10 September 2020
ER -