Takeshi Hara

TL;DR: This study investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images and found the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation.

TL;DR: A novel approach for automatic segmentation of anatomical structures on 3D CT images by voting from a fully convolutional network (FCN), which accomplishes an end-to-end, voxel-wise multiple-class classification to map each voxels in a CT image directly to an anatomical label.

TL;DR: The experimental results for two representative databases show that the proposed method is strongly correlated to subjective quality evaluations and competitive to the state-of-the-art NR-IQA methods.

TL;DR: The aim of this study was to determine the carrier rate of hepatitis virus in patients with haematological malignancies and the incidence of liver injury in these patients following chemotherapy and after lamivudine became available in this institution, the incidence in HBV carriers was reduced.

TL;DR: Sarcopenia is an independent prognostic factor in male patients with DLBCL who received the R-CHOP or R-THP-COP regimen between June 2004 and May 2014 and was measured by the analysis of CT images at the L3 level before treatment.