40 Result(s)
within
Artificial Intelligence
Health Informatics
**ya Sato
Chapter
Conference Paper
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Chapter and Conference Paper
Unsupervised Domain Adaptation with Adversarial Learning for Liver Tumors Detection in Multi-phase CT Images
Automatic and accurate liver tumor detection in multi-phase CT images plays an important role in computer-aided diagnosis. A deep learning-based AI system requires a large amount of training data, but sufficie...
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Chapter and Conference Paper
4D-Foot: A Fully Automated Pipeline of Four-Dimensional Analysis of the Foot Bones Using Bi-plane X-Ray Video and CT
We aim to elucidate the mechanism of the foot by automated measurement of its multiple bone movement using 2D-3D registration of bi-plane x-ray video and a stationary 3D CT. Conventional analyses allowed track...
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Chapter and Conference Paper
Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN
CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle structures and diagnose osteonecrosis due to its superior soft tissue contrast. However, MRI has poor contrast for bon...
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Chapter and Conference Paper
Registration-Based Patient-Specific Musculoskeletal Modeling Using High Fidelity Cadaveric Template Model
We propose a method to construct patient-specific musculoskeletal model using a template obtained from a high fidelity cadaver images. Musculoskeletal simulation has been traditionally performed using a string-ty...
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Chapter and Conference Paper
Patient-Specific Skeletal Muscle Fiber Modeling from Structure Tensor Field of Clinical CT Images
We propose an optimization method for estimating patient-specific muscle fiber arrangement from clinical CT. Our approach first computes the structure tensor field to estimate local orientation, then a geometr...
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Chapter and Conference Paper
Separation of Transmitted Light and Scattering Components in Transmitted Microscopy
In transmitted light microscopy, a specimen tends to be observed as unclear. This is caused by a phenomenon that an image sensor captures the sum of these scattered light rays traveled from different paths due...
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Chapter and Conference Paper
Semi-supervised Learning for Biomedical Image Segmentation via Forest Oriented Super Pixels(Voxels)
In this paper, we focus on semi-supervised learning for biomedical image segmentation, so as to take advantage of huge unlabelled data. We observe that there usually exist some homogeneous connected areas of l...
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Chapter and Conference Paper
Vascular Registration in Photoacoustic Imaging by Low-Rank Alignment via Foreground, Background and Complement Decomposition
Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through mul...
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Chapter and Conference Paper
Reconstructing Shape and Appearance of Thin Film Objects with Hyper Spectral Sensor
Modeling the shape and appearance of a thin film object has promising applications such as heritage-modeling and industrial inspections. In the same time, such modeling is a frontier of computer vision and con...
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Chapter and Conference Paper
Interventional Photoacoustic Imaging of the Human Placenta with Ultrasonic Tracking for Minimally Invasive Fetal Surgeries
Image guidance plays a central role in minimally invasive fetal surgery such as photocoagulation of inter-twin placental anastomosing vessels to treat twin-to-twin transfusion syndrome (TTTS). Fetoscopic guida...
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Chapter and Conference Paper
A Registration Approach to Endoscopic Laser Speckle Contrast Imaging for Intrauterine Visualisation of Placental Vessels
Intrauterine interventions such as twin-to-twin transfusion syndrome procedure require accurate map** of the fetal placental vasculature to ensure complete photocoagulation of vascular anastomoses. However, ...
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Chapter and Conference Paper
A Liver Atlas Using the Special Euclidean Group
An atlas is a shape model derived using statistics of a population. Standard models treat local deformations as pure translations and apply linear statistics. They are often inadequate for highly variable anat...
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Chapter and Conference Paper
Asymmetric Feature Representation for Object Recognition in Client Server System
This paper proposes asymmetric feature representation and efficient fitting feature spaces for object recognition in client server system. We focus on the fact that the server-side has more sufficient memory a...
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Chapter and Conference Paper
Color Photometric Stereo Using a Rainbow Light for Non-Lambertian Multicolored Surfaces
This paper presents a novel approach for recovering the shape of non-Lambertian, multicolored objects using two input images. We show that a ring light source with complementary colored lights has the potentia...
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Chapter and Conference Paper
Toward Efficient Acquisition of BRDFs with Fewer Samples
In this paper we propose a novel method for measuring reflectance of isotropic materials efficiently by carefully choosing a set of sampling directions which yields less modeling error. The analysis is based o...
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Chapter and Conference Paper
Deblurring Vein Images and Removing Skin Wrinkle Patterns by Using Tri-band Illumination
We present a new method for enhancing images of blood vessels in skin tissues by using tri-band illumination. Transmitted-light vein images captured by a camera contain an image blur due to light scattering in...
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Chapter and Conference Paper
Automated CT Segmentation of Diseased Hip Using Hierarchical and Conditional Statistical Shape Models
Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge ...
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Chapter and Conference Paper
Abdominal Multi-organ CT Segmentation Using Organ Correlation Graph and Prediction-Based Shape and Location Priors
The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using inten...
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Chapter and Conference Paper
Utilizing Disease-Specific Organ Shape Components for Disease Discrimination: Application to Discrimination of Chronic Liver Disease from CT Data
We describe a method to capture disease-specific components in organ shapes. A statistical shape model, constructed by the principal component analysis (PCA) of organ shapes, is used to define the subspace rep...
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Chapter and Conference Paper
Reconstructing Sequential Patterns without Knowing Image Correspondences
In this paper, we propose a method for reconstructing 3D sequential patterns from multiple images without knowing image correspondences and without calibrating camera sensitivity parameters on intensity. The s...
