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Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Accurate 3D Reconstruction of a Whole Pancreatic Cancer Tumor from Pathology Images with Different Stains

Authors : Mauricio Kugler, Yushi Goto, Naoki Kawamura, Hirokazu Kobayashi, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani

Published in: Computational Pathology and Ophthalmic Medical Image Analysis

Publisher: Springer International Publishing

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Abstract

When applied to 3D image reconstruction, conventional landmark-based registration methods tend to generate unnatural vertical structures due to inconsistencies between the employed model and the real tissue. This paper demonstrates a fully non-rigid image registration method for 3D image reconstruction which considers the spatial continuity and smoothness of each constituent part of the microstructures in the tissue. Corresponding landmarks are detected along the images, defining a set of trajectories, which are smoothed out in order to define a diffeomorphic mapping. The resulting reconstructed 3D image preserves the original tissue architecture, allowing the observation of fine details and structures.

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previous chapter Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image
next chapter Role of Task Complexity and Training in Crowdsourced Image Annotation
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Metadata
Title
Accurate 3D Reconstruction of a Whole Pancreatic Cancer Tumor from Pathology Images with Different Stains
Authors
Mauricio Kugler
Yushi Goto
Naoki Kawamura
Hirokazu Kobayashi
Tatsuya Yokota
Chika Iwamoto
Kenoki Ohuchida
Makoto Hashizume
Hidekata Hontani
Copyright Year
2018
Book
Computational Pathology and Ophthalmic Medical Image Analysis

Print ISBN: 978-3-030-00948-9

Electronic ISBN: 978-3-030-00949-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00949-6_5

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