TY - GEN
T1 - Learning Erosional Probability Maps for Nuclei Instance Segmentation
AU - Huang, Zhongyi
AU - Ding, Yao
AU - Geng, Ruizhe
AU - He, Hongliang
AU - Huang, Xiansong
AU - Chen, Jie
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Nuclei instance segmentation is an essential but challenging topic in computer vision and medical image analysis due to the ambiguity of nuclei boundaries, the severe border adhesions, and the diversity of nuclei shapes and sizes. In this paper, we propose a simple yet effective proposal-free framework for accurate nuclei instance segmentation. Specifically, we adopt a deep regression model to learn the mapping from the input images to the nuclei erosional inside probability maps and obtain the final instance segmentation results through straightforward dilation post-processing. We evaluate our framework on the open available 2018 Data Science Bowl nuclei instance segmentation dataset and outperform the state-of-The-Art methods.
AB - Nuclei instance segmentation is an essential but challenging topic in computer vision and medical image analysis due to the ambiguity of nuclei boundaries, the severe border adhesions, and the diversity of nuclei shapes and sizes. In this paper, we propose a simple yet effective proposal-free framework for accurate nuclei instance segmentation. Specifically, we adopt a deep regression model to learn the mapping from the input images to the nuclei erosional inside probability maps and obtain the final instance segmentation results through straightforward dilation post-processing. We evaluate our framework on the open available 2018 Data Science Bowl nuclei instance segmentation dataset and outperform the state-of-The-Art methods.
KW - cell boundary ambiguity
KW - deep learning
KW - erosional probability map
KW - nuclei instance segmentation
UR - http://www.scopus.com/inward/record.url?scp=85092205430&partnerID=8YFLogxK
U2 - 10.1109/MIPR49039.2020.00068
DO - 10.1109/MIPR49039.2020.00068
M3 - Conference contribution
AN - SCOPUS:85092205430
T3 - Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
SP - 297
EP - 302
BT - Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
Y2 - 6 August 2020 through 8 August 2020
ER -