TY - GEN
T1 - Registration between histopathological images with different stains and an MRI Image of pancreatic cancer tumor
AU - Hontani, Hidekata
AU - Goto, Yushi
AU - Tamura, Yuki
AU - Shimomura, Tomoshige
AU - Kawamura, Naoki
AU - Kobayashi, Hirokazu
AU - Kugler, Mauricio
AU - Yokota, Tatsuya
AU - Iwamoto, Chika
AU - Ohuchida, Kenoki
AU - Hashizume, Makoto
AU - Katagiri, Takahiro
AU - Sei, Tomonari
AU - Shimizu, Akinobu
N1 - Funding Information:
This work was supported (in part) by JSPS Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy) JSPS KAKENHI Grant Number 26108003.
Publisher Copyright:
© 2019 SPIE.
PY - 2019
Y1 - 2019
N2 - In this paper, we report on the construction of a pancreatic tumor model that represents the relationship between the tumor growth and the micro anatomical structures. The former, the tumor growth, is described by referring to the temporal series of MRI images of the whole body and the latter, the micro structures of the tumor, is described by a spatial series of microscopic images of thin-sections sliced from the extracted pancreatic tumor. For the model construction, we developed new non-rigid registration methods for (1) accurate description of tumor growth, (2) reconstruction of 3D microscopic images, and (3) registration between an MRI image and corresponding microscopic images. In addition, we constructed a neural network that can generate a set of fake microscopic image patches of a pancreatic tumor that corresponds to each voxel inside the tumor region in an MRI image. The outlines of the methods are introduced and some examples of experimental results are demonstrated.
AB - In this paper, we report on the construction of a pancreatic tumor model that represents the relationship between the tumor growth and the micro anatomical structures. The former, the tumor growth, is described by referring to the temporal series of MRI images of the whole body and the latter, the micro structures of the tumor, is described by a spatial series of microscopic images of thin-sections sliced from the extracted pancreatic tumor. For the model construction, we developed new non-rigid registration methods for (1) accurate description of tumor growth, (2) reconstruction of 3D microscopic images, and (3) registration between an MRI image and corresponding microscopic images. In addition, we constructed a neural network that can generate a set of fake microscopic image patches of a pancreatic tumor that corresponds to each voxel inside the tumor region in an MRI image. The outlines of the methods are introduced and some examples of experimental results are demonstrated.
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U2 - 10.1117/12.2522052
DO - 10.1117/12.2522052
M3 - Conference contribution
AN - SCOPUS:85063903925
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - International Forum on Medical Imaging in Asia 2019
A2 - Fujita, Hiroshi
A2 - Kim, Jong Hyo
A2 - Lin, Feng
PB - SPIE
T2 - International Forum on Medical Imaging in Asia 2019
Y2 - 7 January 2019 through 9 January 2019
ER -
