@InProceedings{10.1007/978-3-030-00949-6_32,

author="Yin, Pengshuai
and Tan, Mingkui
and Min, Huaqing
and Xu, Yanwu
and Xu, Guanghui
and Wu, Qingyao
and Tong, Yunfei
and Risa, Higashita
and Liu, Jiang",

editor="Stoyanov, Danail
and Taylor, Zeike
and Ciompi, Francesco
and Xu, Yanwu
and Martel, Anne
and Maier-Hein, Lena
and Rajpoot, Nasir
and van der Laak, Jeroen
and Veta, Mitko
and McKenna, Stephen
and Snead, David
and Trucco, Emanuele
and Garvin, Mona K.
and Chen, Xin Jan
and Bogunovic, Hrvoje",

title="Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images",

booktitle="Computational Pathology and Ophthalmic Medical Image Analysis",

year="2018",

publisher="Springer International Publishing",

address="Cham",

pages="269--276",

abstract="We propose a pipeline for automatically segmenting cortex and nucleus in a 360-degree anterior segment optical coherence tomography (AS-OCT) image. The proposed pipeline consists of a U-shaped network followed by a shape template. The U-shaped network predicts a mask for cortex and nucleus. However, the boundary between cortex and nucleus is weak, so that the boundary of the prediction is an irregular shape and does not satisfy the physiological structure of nucleus. To address this problem, in the second step, we design a shape template according to the physiological structure of nucleus to refine the boundary. Our method integrates both appearance and structure information. The accuracy is measured by the normalized mean squared error (NMSE) between ground truth line and predicted line. We achieve NMSE 7.09/7.94 for nucleus top/bottom boundary and 2.49/2.43 for cortex top/bottom boundary.",
isbn="978-3-030-00949-6"
}