A5103062935- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Medical Image Segmentation Techniques
- Particle physics theoretical and experimental studies
- Dark Matter and Cosmic Phenomena
- Advanced X-ray and CT Imaging
- Colorectal Cancer Screening and Detection
- AI in cancer detection
- Advanced Image Processing Techniques
- Astrophysics and Cosmic Phenomena
- Advanced MRI Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- Advanced Neural Network Applications
- Nuclear physics research studies
- COVID-19 diagnosis using AI
- High-Energy Particle Collisions Research
- Cardiac Imaging and Diagnostics
- Quantum Chromodynamics and Particle Interactions
- Colorectal Cancer Surgical Treatments
- Image and Signal Denoising Methods
- Advanced Vision and Imaging
- Medical Imaging and Analysis
- Radiation Therapy and Dosimetry
- Image Processing Techniques and Applications
- Vitamin C and Antioxidants Research
University of Shizuoka
2023-2025
Japan Aerospace Exploration Agency
2023
Nagoya University
1998-2022
Bellingham Technical College
2018
Niigata City General Hospital
2018
Kamineni Institute of Dental Sciences
2017
Government Dental College and Hospital
2017
Yenepoya University
2016
Optical Sciences Company (United States)
2010
Aichi University of Education
1991-2009
An extensive outbreak of staphylococcal food poisoning occurred in Kansai district Japan. As many as 13,420 cases frequently ingested dairy products manufactured by a factory Osaka City. The main ingredient these was powdered skim milk Hokkaido. Staphylococcal enterotoxin A (SEA) (< or = 0.38 ng/ml) detected low-fat and approx. 3.7 ng/g milk. total intake SEA per capita estimated mostly at 20-100 ng. assumed attack rate considerably lower than those reported previous outbreaks. exposed least...
Two unusually-high-multiplicity interactions of high-energy heavy nuclei are observed in a balloon-borne emulsion chamber: A Si + Ag Br event (4 TeV/nucleon) and Ca C (100 TeV/nucleon), with 1015 760 charged particles, respectively. The multiplicities rapidity distributions favor the multichain model but not wounded-nucleon superposition model. high average ${P}_{T}$ (550-700 MeV/c) fluctuations events readily understood terms any models.
We have studied stars in nuclear emulsion due to the capture at rest of Ξ- hyperons produced (K-, K+) reaction. The sequential weak decay a double hypernucleus (nucleus with S = -2) has been directly observed. is assigned as either 10ΛΛBe or 13ΛΛB. This assignment excludes existence H dibaryon lighter than 2203.7 ±0.7 MeV/c2.
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi-class FCN trained on manually labeled CT scans seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models. To end, propose two-stage, coarse-to-fine...
One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area research, but task proven very challenging due to large variation anatomy across different patients. However, recent advances deep learning have made it possible significantly improve performance image recognition and methods field computer vision. Due data driven approaches hierarchical feature frameworks, these can be translated images without much...
This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. However, most of the recent methods rely on supervised learning, which requires large amounts manually annotated data. Thus, it is challenging these to cope with growing amount proposes unified approach deep representation learning and clustering Our proposed consists two phases. In first phase, we learn feature...
The S = -2 hypernuclear states formed through (K-, K+) reactions have been studied by using the 1.66 GeV/c K- beam provided KEK Proton Synchrotron. Investigation has done on stars due to capture of Ξ- hyperons at rest in nuclear emulsion, which were produced reactions. We observed a clear case an absorption hyperon nucleus followed back-to-back emission two single-Λ hypernuclei.
Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process image including whole pancreas produce an automatic segmentation. We investigate two FCN architecture; one with concatenation summation skip connections to decoder part network. evaluate our on dataset from clinical trial gastric...
Abstract Purpose The paper introduces a novel two-step network based on semi-supervised learning for intestine segmentation from CT volumes. folds in the abdomen with complex spatial structures and contact neighboring organs that bring difficulty accurate labeling at pixel level. We propose multi-dimensional consistency method to reduce insufficient results caused by limited labeled dataset. Methods designed two-stage model segment intestine. In stage 1, 2D Swin U-Net is trained using data...
PurposeWe present a semi-supervised method for intestine segmentation to assist clinicians in diagnosing intestinal diseases. Accurate is essential planning treatments conditions such as obstruction. Although fully supervised learning performs well with abundant labeled data, the complexity of intestine's spatial structure makes labeling time-intensive, resulting limited data. We propose 3D network bidirectional teaching strategy enhance accuracy using this dataset.MethodThe proposed...
We propose a novel mediastinal lymph node detection and segmentation method from chest CT volumes based on fully convolutional networks (FCNs). Most methods are filters for blob-like structures, which not specific nodes. The 3D U-Net is recent example of the state-of-the-art FCNs. can be trained to learn appearances nodes in order output likelihood maps input volumes. However, it prone oversegmentation each due strong data imbalance between remaining part To moderate balance sizes target...
Deep learning-based methods achieved impressive results for the segmentation of medical images. With development 3D fully convolutional networks (FCNs), it has become feasible to produce improved multi-organ computed tomography (CT) The using deep not only depend on choice architecture, but also strongly rely loss function. In this paper, we present a discussion influence Dice-based functions multi-class organ dataset abdominal CT volumes. We investigated three different types weighting Dice...
Abstract This research was in order to follow the periodic fluctuation of lipid peroxidation by a new method rats exposed nitrogen dioxide. Wistar male were examined for as demonstrated ethane exhalation. In continuously 10 ppm dioxide 2 weeks, amount exhaled fluctuated complex manner during exposure. Ethane exhalation decreased slightly after first day exposure and then increased rapidly. The maximal values observed fourth gradually initial level. Furthermore, activity glutathione...
Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data can be beneficial for biomedical research and clinical applications. However, it is a challenging task. Recent advances in deep learning showed feasibility to use 3-D fully convolutional networks (FCN) voxel-wise dense predictions single (SECT). In this paper, we proposed 3D FCN based method automatic DECT. The work was on cascaded general model major organs trained large set SECT data. We preprocessed DECT...
This paper presents a novel method for unsupervised segmentation of pathology images. Staging lung cancer is major factor prognosis. Measuring the maximum dimensions invasive component in images an essential task. Therefore, image methods visualizing extent and noninvasive components on could support pathological examination. However, it challenging most recent that rely supervised learning to cope with unlabeled In this paper, we propose unified approach representation clustering...