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292 results on '"Ikeno, Hidetoshi"'

16. Development of Invertebrate Brain Platform: Management of Research Resources for Invertebrate Neuroscience and Neuroethology

Author

Ikeno, Hidetoshi, Kanzaki, Ryohei, Aonuma, Hitoshi, Takahata, Masakazu, Mizunami, Makoto, Yasuyama, Kouji, Matsui, Nobuyuki, Yokohari, Fumio, Usui, Shiro, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ishikawa, Masumi, editor, Doya, Kenji, editor, Miyamoto, Hiroyuki, editor, and Yamakawa, Takeshi, editor

Published
2008
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17. Japanese Neuroinformatics Node and Platforms

Author

Usui, Shiro, Furuichi, Teiichi, Miyakawa, Hiroyoshi, Ikeno, Hidetoshi, Nagao, Soichi, Iijima, Toshio, Kamiyama, Yoshimi, Isa, Tadashi, Suzuki, Ryoji, Ishikane, Hiroshi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ishikawa, Masumi, editor, Doya, Kenji, editor, Miyamoto, Hiroyuki, editor, and Yamakawa, Takeshi, editor

Published
2008
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24. A root auto tracing and analysis (ARATA): An automatic analysis software for detecting fine roots in images from flatbed optical scanners

Author

90444570, Yabuki, Arata, Ikeno, Hidetoshi, Dannoura, Masako, 90444570, Yabuki, Arata, Ikeno, Hidetoshi, and Dannoura, Masako

Abstract

1. Buried scanners are often used to study fine root dynamics by continuously observing them from the images taken at a fixed point. Accordingly, software have been developed to support operators to quantitatively analyse fine roots from scanned images. However, image processing is still time-consuming work. 2. Deep learning has achieved impressive results as a method for recognising objects in pixel units. In this study, we attempted to automate the image analysis of fine roots using convolutional neural network. 3. Using a root auto tracing and analysis (ARATA), we succeeded in extracting fine roots from scanned images and calculated projected area of fine roots for long-term dynamics. 4. Our software enables the automatic processing of scanned images acquired at various study sites and accelerates the study of fine root dynamics over extended time periods.

Published
2022

42. A quantitative evaluation of soil mass held by tree roots

Author

Tanikawa, Toko, primary, Ikeno, Hidetoshi, additional, Todo, Chikage, additional, Yamase, Keitaro, additional, Ohashi, Mizue, additional, Okamoto, Toru, additional, Mizoguchi, Takeo, additional, Nakao, Katsuhiro, additional, Kaneko, Shinji, additional, Torii, Atsushi, additional, Inagaki, Yoshiyuki, additional, Nakanishi, Asami, additional, and Hirano, Yasuhiro, additional

Published
2020
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48. A Segmentation Scheme for Complex Neuronal Arbors and Application to Vibration Sensitive Neurons in the Honeybee Brain

Author

Ikeno, Hidetoshi, Kumaraswamy, Ajayrama, Kai, Kazuki, Wachtler, Thomas, and Ai, Hiroyuki

Subjects
0301 basic medicine, Masking (art), reconstruction, Interneuron, Computer science, Biomedical Engineering, Neuroscience (miscellaneous), Image processing, insect brain, Tracing, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Software, Encoding (memory), confocal laser scanning microscopic image, medicine, Methods, Segmentation, Projection (set theory), lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, TRACE (psycholinguistics), business.industry, Pattern recognition, complex neuron segmentation, image processing, Computer Science Applications, medicine.anatomical_structure, 030104 developmental biology, Neuron, Artificial intelligence, business, 030217 neurology & neurosurgery, Neuroscience
Abstract

The morphology of a neuron is strongly related to its physiological properties, application of logical product and thus to information processing functions. Optical microscope images are widely used for extracting the structure of neurons. Although several approaches have been proposed to trace and extract complex neuronal structures from microscopy images, available methods remain prone to errors. In this study, we present a practical scheme for processing confocal microscope images and reconstructing neuronal structures. We evaluated this scheme using image data samples and associated \textquotedblgold standard\textquotedbl reconstructions from the BigNeuron Project. In these samples, dendritic arbors belonging to multiple projection branches of the same neuron overlapped in space, making it difficult to automatically and accurately trace their structural connectivity. Our proposed scheme, which combines several software tools for image masking and filtering with an existing tool for dendritic segmentation and tracing, outperformed state-of-the-art automatic methods in reconstructing such neuron structures. For evaluating our scheme, we applied it to a honeybee local interneuron, DL-Int-1, which has complex arbors and is considered to be a critical neuron for encoding the distance information indicated in the waggle dance of the honeybee.

Published
2018
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49. Measuring the Flight Ability of the Ambrosia Beetle, Platypus Quercivorus (Murayama), Using a Low-Cost, Small, and Easily Constructed Flight Mill

Author

Okada, Ryuichi, Pham, Duy Long, Ito, Yasuto, Yamasaki, Michimasa, and Ikeno, Hidetoshi

Subjects
Flight, Animal, Animals, Weevils, Wings, Animal, Environmental Sciences, Biomechanical Phenomena
Abstract

The ambrosia beetle, Platypus quercivorus (Murayama), is the vector of a fungal pathogen that causes mass mortality of Fagaceae trees (Japanese oak wilt). Therefore, knowing the dispersal capacity may help inform trapping/tree removal efforts to prevent this disease more effectively. In this study, we measured the flight velocity and duration and estimated the flight distance of the beetle using a newly developed flight mill. The flight mill is low cost, small, and constructed using commonly available items. Both the flight mill arm and its vertical axis comprise a thin needle. A beetle specimen is glued to one tip of the arm using instant glue. The other tip is thick due to being covered with plastic, thus it facilitates the detection of rotations of the arm. The revolution of the arm is detected by a photo sensor mounted on an infrared LED, and is indicated by a change in the output voltage when the arm passed above the LED. The photo sensor is connected to a personal computer and the output voltage data are stored at a sampling rate of 1 kHz. By conducting experiments using this flight mill, we found that P. quercivorus can fly at least 27 km. Because our flight mill comprises cheap and small ordinary items, many flight mills can be prepared and used simultaneously in a small laboratory space. This enables experimenters to obtain a sufficient amount of data within a short period.

Published
2018

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