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2019 | OriginalPaper | Chapter

Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNN

Authors : Xinpeng Xie, Yuexiang Li, Menglu Zhang, Linlin Shen

Published in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Publisher: Springer International Publishing

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Abstract

Nuclei segmentation plays an import role in histopathology images analysis. Deep learning approaches have shown its strength for histopathology images processing in various studies. In this paper, we proposed a novel deep learning framework for automatic nuclei segmentation. The framework adopts the Mask R-CNN as backbone and employs structure-preserving color normalization (SPCN) and watershed for pre- and post-processing. The proposed framework achieved a Dice score of 90.46% on the validation set, which demonstrates its competing segmentation performance.

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previous chapter A Combined Radio-Histological Approach for Classification of Low Grade Gliomas

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Title: Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNN
Authors: Xinpeng Xie
Yuexiang Li
Menglu Zhang
Linlin Shen
Publisher: Springer International Publishing
Book: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-030-11722-1

Electronic ISBN: 978-3-030-11723-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-11723-8_43

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