TY - GEN
T1 - A Comparative Study of U-Net Topologies for Background Removal in Histopathology Images
AU - Riasatian, Abtin
AU - Rasoolijaberi, Maral
AU - Babaei, Morteza
AU - Tizhoosh, H. R.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - During the last decade, the digitization of pathology has gained considerable momentum. Digital pathology offers many advantages including more efficient workflows, easier collaboration as well as a powerful venue for telepathology. At the same time, applying Computer-Aided Diagnosis (CAD) on Whole Slide Images (WSIs) has received substantial attention as a direct result of the digitization. The first step in any image analysis is to extract the tissue. Hence, background removal is an essential prerequisite for efficient and accurate results for many algorithms. In spite of the obvious discrimination for human operator, the identification of tissue regions in WSIs could be challenging for computers mainly due to the existence of color variations and artifacts. Moreover, some cases such as alveolar tissue types, fatty tissues, and tissues with poor staining are difficult to detect. In this paper, we perform experiments on U-Net architecture with different network backbones (different topologies) to remove the background as well as artifacts from WSIs in order to extract the tissue regions. We compare a wide range of backbone networks including MobileNet, VGG16, EfficientNet-B3, ResNet50, ResNext101 and DenseNet121. We trained and evaluated the network on a manually labeled subset of The Cancer Genome Atlas (TCGA) Dataset. EfficientNet-B3 and MobileNet by almost 99% sensitivity and specificity reached the best results.
AB - During the last decade, the digitization of pathology has gained considerable momentum. Digital pathology offers many advantages including more efficient workflows, easier collaboration as well as a powerful venue for telepathology. At the same time, applying Computer-Aided Diagnosis (CAD) on Whole Slide Images (WSIs) has received substantial attention as a direct result of the digitization. The first step in any image analysis is to extract the tissue. Hence, background removal is an essential prerequisite for efficient and accurate results for many algorithms. In spite of the obvious discrimination for human operator, the identification of tissue regions in WSIs could be challenging for computers mainly due to the existence of color variations and artifacts. Moreover, some cases such as alveolar tissue types, fatty tissues, and tissues with poor staining are difficult to detect. In this paper, we perform experiments on U-Net architecture with different network backbones (different topologies) to remove the background as well as artifacts from WSIs in order to extract the tissue regions. We compare a wide range of backbone networks including MobileNet, VGG16, EfficientNet-B3, ResNet50, ResNext101 and DenseNet121. We trained and evaluated the network on a manually labeled subset of The Cancer Genome Atlas (TCGA) Dataset. EfficientNet-B3 and MobileNet by almost 99% sensitivity and specificity reached the best results.
KW - Convolutional Networks
KW - Histopathology
KW - Tissue Segmentation
KW - U-Net
KW - artifact removal
UR - http://www.scopus.com/inward/record.url?scp=85093864733&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093864733&partnerID=8YFLogxK
U2 - 10.1109/IJCNN48605.2020.9207018
DO - 10.1109/IJCNN48605.2020.9207018
M3 - Conference contribution
AN - SCOPUS:85093864733
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020
Y2 - 19 July 2020 through 24 July 2020
ER -