TY - GEN
T1 - Automatic anterior chamber angle structure segmentation in AS-OCT image based on label transfer
AU - Fu, Huazhu
AU - Xu, Yanwu
AU - Wong, Damon Wing Kee
AU - Liu, Jiang
AU - Baskaran, Mani
AU - Perera, Shamira A.
AU - Aung, Tin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - The anterior chamber angle (ACA) plays an important role for diagnosis and treatment of angle-closure glaucoma. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging is qualitative and quantitative assessment for the ACA structure. In this paper, we propose a novel fully automatic segmentation method for anterior chamber angle structure in AS-OCT. In our method, the initial labels are obtained by using label transfer from the AS-OCT reference dataset. Then, these labels are refined and utilized as the landmarks to support the structure segmentation. Finally, the major clinical structures: corneal boundary, iris region, and trabecular-iris contact, are extracted as the segmentation result. Experiments show that our proposed method achieve the satisfactory segmentation performance on the clinical AS-OCT dataset. Our proposed method has potential in the applications of clinical ACA parameter measurement and automatic glaucoma classification.
AB - The anterior chamber angle (ACA) plays an important role for diagnosis and treatment of angle-closure glaucoma. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging is qualitative and quantitative assessment for the ACA structure. In this paper, we propose a novel fully automatic segmentation method for anterior chamber angle structure in AS-OCT. In our method, the initial labels are obtained by using label transfer from the AS-OCT reference dataset. Then, these labels are refined and utilized as the landmarks to support the structure segmentation. Finally, the major clinical structures: corneal boundary, iris region, and trabecular-iris contact, are extracted as the segmentation result. Experiments show that our proposed method achieve the satisfactory segmentation performance on the clinical AS-OCT dataset. Our proposed method has potential in the applications of clinical ACA parameter measurement and automatic glaucoma classification.
UR - http://www.scopus.com/inward/record.url?scp=85009075014&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7590942
DO - 10.1109/EMBC.2016.7590942
M3 - Conference contribution
C2 - 28268561
AN - SCOPUS:85009075014
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1288
EP - 1291
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Y2 - 16 August 2016 through 20 August 2016
ER -