Hidetaka Arimura

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patient

This paper reviews the basics and recent researches of computer-aided diagnosis (CAD) systems for assisting neuroradiologists in detection of brain diseases, e.g., asymptomatic unruptured aneurysms, Alzheimer's disease, vascular dementia, and multiple sclerosis (MS), in magnetic resonance (MR) images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and suppo

We have improved a computerized scheme for the detection of intracranial aneurysms for three-dimensional (3-D) magnetic resonance angiography (MRA) by the use of image features of small protrusions extracted based on a shape-based difference image (SBDI) technique. Initial candidates were identified by use of a multiple gray-level thresholding technique in dot enhanced images, and by finding short branches in skeleton images. Image features related to aneurysms were determined based on candidate

This study has revealed the potential of Symlet and Biorthogonal mother wavelets in the survival prediction of lung cancer patients by using WDB radiomic features in CT images.

This study developed a radiomics-based predictive model for radiation-induced pneumonitis (RP) after lung cancer stereotactic body radiation therapy (SBRT) on pretreatment planning computed tomography (CT) images. For the RP prediction models, 275 non-small-cell lung cancer patients consisted of 245 training (22 with grade ≥ 2 RP) and 30 test cases (8 with grade ≥ 2 RP) were selected. A total of 486 radiomic features were calculated to quantify the RP texture patterns reflecting radiation-induce

The purpose of this study was to develop a computerized method for estimation of the location of a lung tumor in cine images on an electronic portal imaging device (EPID) without implanted markers during stereotactic body radiotherapy (SBRT). Each tumor region was segmented in the first EPID cine image, i.e., reference portal image, based on a multiple-gray level thresholding technique and a region growing technique, and then the image including the tumor region was cropped as a 'tumor template'

In picture archiving and communications systems (PACS) or digital archiving systems, the information on the posteroanterior (PA) and lateral views for chest radiographs is often not recorded or is recorded incorrectly. However, it is necessary to identify the PA or lateral view correctly and automatically for quantitative analysis of chest images for computer-aided diagnosis. Our purpose in this study was to develop a computerized method for correctly identifying either PA or lateral views of ch

The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.

The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) images using dense V-networks (DVNs) that offer more advantages in segmenting smaller structures than conventional V-networks. Regions of interest (ROI) with dimensions of 50 × 50 × 6-72 pixels in the planning CT images were cropped based on the GTV centroids when applying stereotactic body radiotherapy (SBRT) to patients. Segmentation accuracy of G