Publications by authors named "Ken'ichi Morooka"

Background: Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED.

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A novel neural network called the isomorphic mesh generator (iMG) is proposed to generate isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects exhibit a unified mesh structure, despite objects belonging to different classes. This unified representation enables various modern deep neural networks (DNNs) to easily handle surface models without requiring additional pre-processing.

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Article Synopsis
  • - A study developed an AI model to predict the recurrence of colorectal cancer after surgery by analyzing digitized pathological slides from 471 patients.
  • - The model showed a strong ability to classify patients' risk for recurrence with an area under the curve of 0.7245, indicating it can differentiate between better and worse survival outcomes.
  • - High scores from the model were significantly linked to a worse chance of recurrence, independent of other factors, suggesting it could assist in treatment decisions like the need for adjuvant chemotherapy.
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Early intravesical recurrence after transurethral resection of bladder tumors (TURBT) is often caused by overlooking of tumors during TURBT. Although narrow-band imaging and photodynamic diagnosis were developed to detect more tumors than conventional white-light imaging, the accuracy of these systems has been subjective, along with poor reproducibility due to their dependence on the physician's experience and skills. To create an objective and reproducible diagnosing system, we aimed at assessing the utility of artificial intelligence (AI) with Dilated U-Net to reduce the risk of overlooked bladder tumors when compared with the conventional AI system, termed U-Net.

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In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images, there are organs, called non-target organs, which NPC never invades.

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Background And Objective: This paper proposes a new method for mapping surface models of human organs onto target surfaces with the same genus as the organs.

Methods: In the proposed method, called modified Self-organizing Deformable Model (mSDM), the mapping problem is formulated as the minimization of an objective function which is defined as the weighted linear combination of four energy functions: model fitness, foldover-free, landmark mapping accuracy, and geometrical feature preservation. Further, we extend mSDM to speed up its processes, and call it Fast mSDM.

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The application of assistive technologies for elderly people is one of the most promising and interesting scenarios for intelligent technologies in the present and near future. Moreover, the improvement of the quality of life for the elderly is one of the first priorities in modern countries and societies. In this work, we present an informationally structured room that is aimed at supporting the daily life activities of elderly people.

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This paper describes a new method of measuring the position of everyday objects and a robot on the floor using distance and reflectance acquired by laser range finder (LRF). The information obtained by this method is important for a service robot working in a human daily life environment. Our method uses only one LRF together with a mirror installed on the wall.

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This paper presents a navigation system for minimally invasive surgery, especially laparoscopic surgery in which operates in abdomen. Conventional navigation systems show virtual images by superimposing models of target tissues on real endoscopic images. Since soft tissues within the abdomen are deformed during the surgery, the navigation system needs to provide surgeons reliable information by deforming the models according to their biomechanical behavior.

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Research on the human brain has undoubted significance, but our knowledge on its detailed morphology is still limited. We have developed a simple method for reconstruction of large-sized brain tissues of the human. Fixed brains were cut into blocks (maximum size 7 cm x 7 cm x 1 cm), embedded and postfixed in gelatin just one overnight before obtaining complete serial sections with a vibrating microtome.

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This paper presents a new method for simulating the deformation of organ models by using a neural network. The proposed method is based on the idea proposed by Chen et al. that a deformed model can be estimated from the superposition of basic deformation modes.

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