no code implementations • 20 Oct 2020 • Tong Zheng, Hirohisa ODA, Masahiro Oda, Shota NAKAMURA, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Kensaku MORI
Unsupervised SR methods are required that do not need paired LR and HR images.
no code implementations • 7 Apr 2020 • Tong ZHENG, Hirohisa ODA, Takayasu MORIYA, Takaaki SUGINO, Shota NAKAMURA, Masahiro Oda, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Kensaku MORI
This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes.
no code implementations • 30 Dec 2019 • Tong Zheng, Hirohisa ODA, Takayasu MORIYA, Shota NAKAMURA, Masahiro Oda, Masaki MORI, Horitsugu Takabatake, Hiroshi NATORI, Kensaku MORI
This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes.
no code implementations • 11 Apr 2018 • Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI
In this paper, we propose a unified approach to unsupervised representation learning and clustering for pathology image segmentation.
no code implementations • 11 Apr 2018 • Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI
This paper presents a novel unsupervised segmentation method for 3D medical images.
no code implementations • 27 Feb 2017 • Holger R. Roth, Kai Nagara, Hirohisa ODA, Masahiro Oda, Tomoshi Sugiyama, Shota NAKAMURA, Kensaku MORI
The alignment of clinical CT with $\mu$CT will allow further registration with even finer resolutions of $\mu$CT (up to 10 $\mu$m resolution) and ultimately with histopathological microscopy images for further macro to micro image fusion that can aid medical image analysis.