Panoptic segmentation with an end-to-end cell R-CNN for pathology image analysis

Donghao Zhang, Yang Song, Dongnan Liu, Haozhe Jia, Siqi Liu, Yong Xia, Heng Huang, Weidong Cai

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

43 Citations (Scopus)


The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers. In order to obtain the quantitative morphological information, we present an end-to-end network for panoptic segmentation of pathology images. Recently, many methods have been proposed, focusing on the semantic-level or instance-level cell segmentation. Unlike existing cell segmentation methods, the proposed network unifies detecting, localizing objects and assigning pixel-level class information to regions with large overlaps such as the background. This unifier is obtained by optimizing the novel semantic loss, the bounding box loss of Region Proposal Network (RPN), the classifier loss of RPN, the background-foreground classifier loss of segmentation Head instead of class-specific loss, the bounding box loss of proposed cell object, and the mask loss of cell object. The results demonstrate that the proposed method not only outperforms state-of-the-art approaches to the 2017 MICCAI Digital Pathology Challenge dataset, but also proposes an effective and end-to-end solution for the panoptic segmentation challenge.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018
Subtitle of host publication21st International Conference, 2018, Proceedings Part II
EditorsAlejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
Place of PublicationCham Switzerland
Number of pages8
ISBN (Electronic)9783030009342
ISBN (Print)9783030009335
Publication statusPublished - 2018
Externally publishedYes
EventMedical Image Computing and Computer-Assisted Intervention 2018 - Granada, Spain
Duration: 16 Sept 201820 Sept 2018
Conference number: 21st (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11071 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMedical Image Computing and Computer-Assisted Intervention 2018
Abbreviated titleMICCAI 2018
Internet address

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