TY - GEN
T1 - Shape Mask Generator
T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
AU - Song, Youyi
AU - Zhu, Lei
AU - Lei, Baiying
AU - Sheng, Bin
AU - Dou, Qi
AU - Qin, Jing
AU - Choi, Kup Sze
N1 - Funding Information:
The work described in this paper is supported by a grant from the Hong Kong Research Grants Council (Project No. 15205919), a grant from the Hong Kong Polytechnic University (Project No. PolyU 152009/18E), a grant from the National Natural Science Foundation of China (Grant No. 61902275), a grant from the Innovative Technology Fund (Grant No. MRP/015/18), and a grant from the General Research Fund (Grant No. PolyU 152006/19E).
Funding Information:
Acknowledgement. The work described in this paper is supported by a grant from the Hong Kong Research Grants Council (Project No. 15205919), a grant from the Hong Kong Polytechnic University (Project No. PolyU 152009/18E), a grant from the National Natural Science Foundation of China (Grant No. 61902275), a grant from the Innovative Technology Fund (Grant No. MRP/015/18), and a grant from the General Research Fund (Grant No. PolyU 152006/19E).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/9
Y1 - 2020/9
N2 - Segmenting overlapping cytoplasm of cervical cells plays a crucial role in cervical cancer screening. This task, however, is rather challenging, mainly because intensity (or color) information in the overlapping region is deficient for recognizing occluded boundary parts. Existing methods attempt to compensate intensity deficiency by exploiting shape priors, but shape priors modeled by them have a weak representation ability, and hence their segmentation results are often visually implausible in shape. In this paper, we propose a conceptually simple and effective technique, called shape mask generator, for segmenting overlapping cytoplasms. The key idea is to progressively refine shape priors by learning so that they can accurately represent most cytoplasms’ shape. Specifically, we model shape priors from shape templates and feed them to the shape mask generator that generates a shape mask for the cytoplasm as the segmentation result. Shape priors are refined by minimizing the ‘generating residual’ in the training dataset, which is designed to have a smaller value when the shape mask generator producing shape masks that are more consistent with the image information. The introduced method is assessed on two datasets, and the empirical evidence shows that it is effective, outperforming existing methods.
AB - Segmenting overlapping cytoplasm of cervical cells plays a crucial role in cervical cancer screening. This task, however, is rather challenging, mainly because intensity (or color) information in the overlapping region is deficient for recognizing occluded boundary parts. Existing methods attempt to compensate intensity deficiency by exploiting shape priors, but shape priors modeled by them have a weak representation ability, and hence their segmentation results are often visually implausible in shape. In this paper, we propose a conceptually simple and effective technique, called shape mask generator, for segmenting overlapping cytoplasms. The key idea is to progressively refine shape priors by learning so that they can accurately represent most cytoplasms’ shape. Specifically, we model shape priors from shape templates and feed them to the shape mask generator that generates a shape mask for the cytoplasm as the segmentation result. Shape priors are refined by minimizing the ‘generating residual’ in the training dataset, which is designed to have a smaller value when the shape mask generator producing shape masks that are more consistent with the image information. The introduced method is assessed on two datasets, and the empirical evidence shows that it is effective, outperforming existing methods.
KW - Cervical cancer screening
KW - Overlapping cytoplasms segmentation
KW - Refining shape priors
KW - Shape mask generator
UR - http://www.scopus.com/inward/record.url?scp=85092766521&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59719-1_62
DO - 10.1007/978-3-030-59719-1_62
M3 - Conference article published in proceeding or book
AN - SCOPUS:85092766521
SN - 9783030597184
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 639
EP - 649
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 October 2020 through 8 October 2020
ER -