Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes

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.

Super-Resolution

Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database

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.

Computed Tomography (CT) Super-Resolution +1

Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

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.

Clustering Image Segmentation +4

Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means

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.

Clustering Image Segmentation +3