Construction of multimodal 3D model of pancreatic cancer tumor

Yushi Goto, Mauricio Kugler, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Histopathological imaging and Magnetic Resonance (MR) are two equally important yet very distinct modalities of medical imaging. The high resolution of the first and the non-invasiveness of the later provide complementary information for medical diagnosis and research. Due to their largely different resolutions, the registration between 3D images of these two modalities is challenging. The objective of this paper is to create a multimodal 3D model of pancreatic cancer tumor by performing the registration of a reconstructed 3D pathological image and an MR image from a KPC mouse. The tumor portions were manually segmented and the 3D pathological image was reconstructed using landmark-based non-linear registration. The process starts by registering the outline of the images using the LDDMM non-linear registration method to match the binary labels of the tumor regions. Next, a non-linear B-spline deformation method based on mutual information maximization is used to register the internal structures of the images. Experimental results show that the overall shape of the tumor and its internal necrosis portion could be correctly registered, although the quality of the manual segmentations affects the accuracy of the registration.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsFeng Lin, Jong Hyo Kim, Hiroshi Fujita
ISBN (Electronic)9781510627758
Publication statusPublished - 2019
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: Jan 7 2019Jan 9 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Forum on Medical Imaging in Asia 2019

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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