TY - GEN
T1 - RBC Semantic Segmentation for Sickle Cell Disease Based on Deformable U-Net
AU - Zhang, Mo
AU - Li, Xiang
AU - Xu, Mengjia
AU - Li, Quanzheng
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the U-Net structure with deformable convolution layers. The U-Net architecture has been shown to offer a precise localization for image semantic segmentation. Moreover, deformable convolution enables the free form deformation of the feature learning process, thus making the whole network more robust to various cell morphologies and image settings. The proposed method is tested on microscopic red blood cell images from patients with sickle cell disease. The results show that U-Net with deformable convolution achieves the highest accuracy for both segmentation and classification tasks, compared with the original U-Net structure and unsupervised methods.
AB - Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the U-Net structure with deformable convolution layers. The U-Net architecture has been shown to offer a precise localization for image semantic segmentation. Moreover, deformable convolution enables the free form deformation of the feature learning process, thus making the whole network more robust to various cell morphologies and image settings. The proposed method is tested on microscopic red blood cell images from patients with sickle cell disease. The results show that U-Net with deformable convolution achieves the highest accuracy for both segmentation and classification tasks, compared with the original U-Net structure and unsupervised methods.
KW - Deformable convolution
KW - RBC semantic segmentation
KW - Sickle cell disease
KW - U-Net
UR - http://www.scopus.com/inward/record.url?scp=85053826838&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053826838&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00937-3_79
DO - 10.1007/978-3-030-00937-3_79
M3 - Conference contribution
AN - SCOPUS:85053826838
SN - 9783030009366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 695
EP - 702
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Fichtinger, Gabor
A2 - Schnabel, Julia A.
A2 - Alberola-López, Carlos
A2 - Davatzikos, Christos
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -