3D Organ Shape Reconstruction from Topogram Images View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2019-05-22

AUTHORS

Elena Balashova , Jiangping Wang , Vivek Singh , Bogdan Georgescu , Brian Teixeira , Ankur Kapoor

ABSTRACT

Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up. However, addressing this problem typically requires performing computed tomography (CT) scanning and complicated post-processing of the resulting scans using slice-by-slice techniques. In this paper, we show that 3D organ shape can be automatically predicted directly from topogram images, which are easier to acquire and have limited exposure to radiation during acquisition, compared to CT scans. We evaluate our approach on the challenging task of predicting liver shape using a generative model. We also demonstrate that our method can be combined with user annotations, such as a 2D mask, for improved prediction accuracy. We show compelling results on 3D liver shape reconstruction and volume estimation on 2129 CT scans (This feature is based on research, and is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed). More... »

PAGES

347-359

Book

TITLE

Information Processing in Medical Imaging

ISBN

978-3-030-20350-4
978-3-030-20351-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-20351-1_26

DOI

http://dx.doi.org/10.1007/978-3-030-20351-1_26

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1115092164


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