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Affiliation Department |
Department of Computer Science
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Title |
Assistant Professor |
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External Link |
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KUGLER Mauricio
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From School
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Federal Technological University of Parana Faculty of Engineering Department of Electronics Graduated
- 2001.02
Country：Brazil
From Graduate School
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Nagoya Institute of Technology Graduate School, Division of Information Engineering Doctor's Course Completed
- 2007.03
Country：Japan
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Federal Technological University of Paraná Graduate School, Division of Engineering Master's Course Completed
- 2003.02
Country：Brazil
External Career
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ESIGELEC Graduate School of Engineering Lecturer
2014.03 - 2014.04
Country：France
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ESIGELEC Graduate School of Engineering Lecturer
2013.11
Country：France
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ESIGELEC Graduate School of Engineering Lecturer
2013.05
Country：France
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ESIGELEC Graduate School of Engineering Lecturer
2012.11 - 2012.12
Country：France
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ESIGELEC Gradiate School of Engineering Lecturer
2012.05
Country：France
Professional Memberships
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日本神経回路学会第22回全国大会実行委員会
2011.09 - 2012.09
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Institute of Electrical and Electronics Engineers (IEEE)
2005.01 - 2014.04
Papers
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Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation Reviewed
Mauricio Kugler, Yushi Goto, Yuki Tamura, Naoki Kawamura, Hirokazu Kobayashi, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Akinobu Shimizu, Hidekata Hontani
International Journal of Computer Assisted Radiology and Surgery 14 ( 12 ) 2047 - 2055 2019.07
Authorship：Lead author Language：English Publishing type：Research paper (scientific journal) Publisher：Springer
Purpose: Histopathological imaging is widely used for the analysis and diagnosis of multiple diseases. Several methods have been proposed for the 3D reconstruction of pathological images, captured from thin sections of a given specimen, which get non-linearly deformed due to the preparation process. The majority of the available methods for registering such images use the degree of matching of adjacent images as the criteria for registration, which can result in unnatural deformations of the anatomical structures. Moreover, most methods assume that the same staining is used for all images, when in fact multiple staining is usually applied in order to enhance different structures in the images.
Methods: This paper proposes a non-rigid 3D reconstruction method based on the assumption that internal structures on the original tissue must be smooth and continuous. Landmarks are detected along anatomical structures using template matching based on normalized cross-correlation (NCC), forming jagged shape trajectories that traverse several slices. The registration process smooths out these trajectories and deforms the images accordingly. Artifacts are automatically handled by using the confidence of the NCC in order to reject unreliable landmarks.
Results: The proposed method was applied to a large series of histological sections from the pancreas of a KPC mouse. Some portions were dyed primarily with HE stain, while others were dyed alternately with HE, CK19, MT and Ki67 stains. A new evaluation method is proposed to quantitatively evaluate the smoothness and isotropy of the obtained reconstructions, both for single and multiple staining.
Conclusions: Experimental results show that the proposed method produces smooth and nearly isotropic 3D reconstructions of pathological images with either single or multiple stains. From these reconstructions, micro-anatomical structures enhanced by different stains can be simultaneously observed.DOI： 10.1007/s11548-019-02019-8
Other Link： https://doi.org/10.1007/s11548-019-02019-8
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Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains Reviewed International journal
Mauricio Kugler, Yushi Goto, Naoki Kawamura, Hirokazu Kobayashi, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani
Proceedings of the 1st Workshop in Computational Pathology - 21st International Conference on Medical Image Computing & Computer Assisted Intervention LNCS11039 35 - 43 2018.09
Authorship：Lead author Language：English Publishing type：Research paper (international conference proceedings)
When applied to 3D image reconstruction, conventional landmark-based registration methods tend to generate unnatural vertical structures due to inconsistencies between the employed model and the real tissue. This paper demonstrates a fully non-rigid image registration method for 3D image reconstruction which considers the spatial continuity and smoothness of each constituent part of the microstructures in the tissue. Corresponding landmarks are detected along the images, defining a set of trajectories, which are smoothed out in order to define a diffeomorphic mapping. The resulting reconstructed 3D image preserves the original tissue architecture, allowing the observation of fine details and structures.
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Spatiotemporal information integration model of pancreatic cancer and human embryo Reviewed
Hidekata Hontani, Mauricio Kugler, Akinobu Shimizu
The CELL 50 ( 1 ) 5 - 8 2018.01
Language：Japanese Publishing type：Research paper (scientific journal)
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Design of a compact sound localization device on a stand-alone FPGA-based platform Reviewed International coauthorship International journal
IEICE Transactions on Information & Systems E99-D ( 11 ) 2682 - 2693 2016.11
Authorship：Lead author Language：English Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers
DOI： 10.1587/transinf.2015EDP7488
Other Link： http://search.ieice.org/bin/summary.php?id=e99-d_11_2682&category=D&year=2016&lang=E&abst=
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Real-time hardware implementation of a sound recognition system with in-field learning Reviewed International coauthorship International journal
Mauricio Kugler, Teemu Tossavainen, Miku Nakatsu, Susumu Kuroyanagi, Akira Iwata
電子情報通信学会論文誌 E99-D ( 7 ) 1885 - 1894 2016.07
Authorship：Lead author Language：English Publishing type：Research paper (scientific journal) Publisher：電子情報通信学会
The development of assistive devices for automated sound recognition is an important field of research and has been receiving increased attention. However, there are still very few methods specifically developed for identifying environmental sounds. The majority of the existing approaches try to adapt speech recognition techniques for the task, usually incurring high computational complexity. This paper proposes a sound recognition method dedicated to environmental sounds, designed with its main focus on embedded applications. The pre-processing stage is loosely based on the human hearing system, while a robust set of binary features permits a simple k-NN classifier to be used. This gives the system the capability of in-field learning, by which new sounds can be simply added to the reference set in real-time, greatly improving its usability. The system was implemented in an FPGA based platform, developed in-house specifically for this application. The design of the proposed method took into consideration several restrictions imposed by the hardware, such as limited computing power and memory, and supports up to 12 reference sounds of around 5.3 s each. Experimental results were performed in a database of 29 sounds. Sensitivity and specificity were evaluated over several random subsets of these signals. The obtained values for sensitivity and specificity, without additional noise, were, respectively, 0.957 and 0.918. With the addition of +6 dB of pink noise, sensitivity and specificity were 0.822 and 0.942, respectively. The in-field learning strategy presented no significant change in sensitivity and a total decrease of 5.4% in specificity when progressively increasing the number of reference sounds from 1 to 9 under noisy conditions. The minimal signal-to-noise ration required by the prototype to correctly recognize sounds was between -8 dB and 3 dB. These results show that the proposed method and implementation have great potential for several real life applications.
DOI： 10.1587/transinf.2015EDP7432
Other Link： http://search.ieice.org/bin/summary.php?id=e99-d_7_1885&category=D&year=2016&lang=E&abst=
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Combined Methodology for Linear Time Series Forecasting Reviewed International journal
Ricardo Moraes Muniz da Silva, Mauricio Kugler, Taizo Umezaki
電気学会論文誌Ｃ（電子・情報・システム部門誌） 15 ( 12 ) 1780 - 1790 2020.10
Language：English Publishing type：Research paper (scientific journal) Publisher：一般社団法人 電気学会
Time series forecasting is an important type of quantitative model used to predict future values given a series of past observations for which the generation process is unknown. Two of the most well‐known methods for the modeling of linear time series are the autoregressive integrated moving average (ARIMA) and the autoregressive fractionally integrated moving average (ARFIMA). For different datasets, the number of past observations necessary for an accurate prediction may vary. Short and long memory dependency problems require different handling, with the ARIMA model being limited to the first, while the ARFIMA model was specifically developed for the latter. Preprocessing techniques and modification on specific components of these models are common approaches used to tackle the memory dependency problem in order to improve their accuracy. However, such solutions are specific to certain datasets. This paper proposes a new method that combines the short and long memory characteristics of the two aforementioned models in order to keep a low accumulative error in several different scenarios. Twelve public time series datasets were used to compare the performance of the proposed method with the original models. The results were also compared with two alternative methods from the literature used to deal with datasets of different memory dependencies. The new approach presented a lower error for the majority of the experiments, failing only for the datasets that contain a large number of features.
DOI： https://doi.org/10.1002/tee.23252
Other Link： https://onlinelibrary.wiley.com/doi/full/10.1002/tee.23252
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Graph neural network for identification of malignant lymphoma subtypes and class activation visualization of cell tissue anomality
Hiromu Tanaka, Mauricio Kugler, Tatsuya Yokota, Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani
2023.01
Language：English Publishing type：Research paper (international conference proceedings)
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Classification of malignant lymphoma cell nuclei by semi-supervised learning
Shingo Koide, Mauricio Kugler, Tatsuya Yokota, Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani
2023.01
Language：English Publishing type：Research paper (international conference proceedings)
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Anomaly Detection for Chest CT Images using Normalizing Flow
Hiroki Tobise, Kugler Mauricio, Tatsuya Yokota, Masahiro Hashimoto, Yoshito Otake, Toshiaki Akashi, Akinobu Shimizu, Hidekata Hontani
2023.01
Language：English Publishing type：Research paper (international conference proceedings)
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Generation of Counterfactual Images to Construct Criteria for Quantitatively Evaluating Subtypes in Malignant Lymphoma Reviewed
Ryoichi Koga, Mauricio Kugler, Tatsuya Yokota, Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani
2023.01
Language：English Publishing type：Research paper (international conference proceedings)
Presentations
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Generation of Counterfactual Images to Construct Criteria for Quantitatively Evaluating Subtypes in Malignant Lymphoma International conference
Ryoichi Koga, Mauricio Kugler, Tatsuya Yokota, Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani
International Forum on Medical Imaging in Asia
Event date： 2023.01
Language：English Presentation type：Oral presentation (general)
Country：Korea, Republic of
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Graph neural network for identification of malignant lymphoma subtypes and class activation visualization of cell tissue anomality International conference
Hiromu Tanaka, Mauricio Kugler, Tatsuya Yokota, Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani
International Forum on Medical Imaging in Asia
Event date： 2023.01
Language：English Presentation type：Oral presentation (general)
Country：Korea, Republic of
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Classification of malignant lymphoma cell nuclei by semi-supervised learning International conference
Shingo Koide, Mauricio Kugler, Tatsuya Yokota, Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani
International Forum on Medical Imaging in Asia
Event date： 2023.01
Language：English Presentation type：Oral presentation (general)
Country：Korea, Republic of
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Anomaly Detection for Chest CT Images using Normalizing Flow International conference
Hiroki Tobise, Kugler Mauricio, Tatsuya Yokota, Masahiro Hashimoto, Yoshito Otake, Toshiaki Akashi, Akinobu Shimizu, Hidekata Hontani
International Forum on Medical Imaging in Asia
Event date： 2023.01
Language：English Presentation type：Oral presentation (general)
Country：Korea, Republic of
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Stain Conversion from H&E to BRAF by using StyleGAN International conference
Toyohiro Maki, Mauricio Kugler, Tatsuya Yokota, satomi hatta, Kunihiro Inai, Hidekata Hontani
International Forum on Medical Imaging in Asia
Event date： 2023.01
Language：English Presentation type：Oral presentation (general)
Country：Korea, Republic of
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Contrastive Learning に基づく次元削減による胸部 CT 画像に対する異常検知
飛世裕貴，クグレ マウリシオ，横田達也，橋本正弘，大竹義人，明石敏昭，清水昭伸，本谷秀堅
日本医用画像工学会大会
Event date： 2022.07
Language：Japanese
Country：Japan
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悪性リンパ腫細胞核画像の低次元表現獲得と特徴の集合に基づくサブタイプ識別器の構築
小出新悟，橋本典明，横田達也，クグレ マウリシオ，大島孝一，三好寛明，永石美晴，竹内一郎，本谷秀堅
日本医用画像工学会大会
Event date： 2022.07
Language：Japanese
Country：Japan
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Graph Neural Networks による悪性リンパ腫のサブタイプ識別と識別根拠となる細胞核の可視化
田中寛武，橋本典明，横田達也，クグレ マウリシオ，大島孝一，三好寛明，永石美晴，竹内一郎，本谷秀堅
日本医用画像工学会大会
Event date： 2022.07
Language：Japanese
Country：Japan
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Contrastive Learning に基づく次元削減による Covid-19 の胸部 CT 画像に対する異常検知
飛世裕貴，クグレ マウリシオ，横田達也，橋本正弘，大竹義人，明石敏昭，清水昭伸，本谷秀堅
MI研究会
Event date： 2022.01
Language：Japanese
Country：Japan
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3次元再構成した膵癌腫瘍の病理顕微鏡画像中の新生血管構造記述
石牧祐香，横田達也，クグレ マウリシオ，本谷秀堅
MI研究会
Event date： 2021.11
Language：Japanese
Country：Japan
Industrial Property Rights
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Soft-stop function in abnormal situations for a transcranial direct-current stimulation device
Masaharu Segawa, Seiji Onishi, Toshiyuki Takagaki, Mauricio Kugler
Application no：2018-3014 Date applied：2018.01
Announcement no：2019-122429 Date announced：2019.07
Country of applicant：Domestic Country of acquisition：Domestic
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Head Mounted Display
Masatoki Suto, Mauricio Kugler, Fukaya Shousuke, Jana Makovníková
Applicant：Nagoya Institute of Technology
Application no：2015-12863 Date applied：2015
Announcement no：2016-139881 Date announced：2016
Country of applicant：Domestic Country of acquisition：Domestic
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Sound source identification method and sound source identification device
Akira Iwata, Mauricio Kugler
Applicant：Nagoya Institute of Technology
Application no：2008-250360 Date applied：2008.09
Announcement no：2010-079188 Date announced：2010.04
Country of applicant：Domestic Country of acquisition：Domestic
Other research activities
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Development of a Wearable Brain Stimulation Device for Gait Rehabilitation
2013.04 - 2015.03
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Sound recognition & visualization using a head mount display
2013.04 - 2014.03
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Sound Watcher: hardware and software development for a consumer product.
2011.01
Awards
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Best APNNA Paper Award
2008.11 International Neural Network Society A Back-Propagation Training Method for Multilayer Pulsed Neural Network using Principle of Duality
Kaname Iwasa, Mauricio Kugler, Susumu Kuroyanagi, Akira Iwata
Award type：Award from international society, conference, symposium, etc. Country：New Zealand
Scientific Research Funds Acquisition Results
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Grant number：22H03613 2022.04 - 2025.03
Grant-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)
Hidekata Hontani, Hiroaki Miyoshi, Ohshima Koichi, Mauricio Kugler, Yokota Tetsuya
Authorship：Coinvestigator(s)
Grant amount：\17290000 （ Direct Cost: \13300000 、 Indirect Cost：\3990000 ）
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Grant number：18H03262 2018.04 - 2021.03
Grant-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)
Hidekata Hontani, Takahiro Katagiri, Mauricio Kugler, Yokota Tatsuya
Grant amount：\17160000 （ Direct Cost: \13200000 、 Indirect Cost：\3960000 ）
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Wearable Brain Stimulation Device for Gait Rehabilitation
Grant number：25560255 2013.04 - 2015.03
Grant-in-Aid for Scientific Research Grant-in-Aid for challenging Exploratory Research
Satoshi Tanaka
Grant amount：\3770000 （ Direct Cost: \2900000 、 Indirect Cost：\870000 ）
This project's main objective is to develop a wearable device that generates the necessary electrical stimulation during such sessions, being wirelessly controlled by the operator by the means of a smartphone or a tablet computer. The hardware device has to generate a constant DC current through the scalp of the subject, independently of its resistance. It has to be light and small, able to be fixed on the back of the head of the subject.
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Visualization of sound using head-mounted display
Grant number：23500649 2011.04 - 2014.03
Grant-in-Aid for Scientific Research Grant-in-Aid for Scientific Research(C)
Masatoki Suto, Mauricio Kugler
Authorship：Coinvestigator(s)
Grant amount：\5590000 （ Direct Cost: \4300000 、 Indirect Cost：\1290000 ）
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Grant number：22560417 2010 - 2012
科学研究費補助金 基盤研究(C)
岩田彰、Mauricio Kugler
Authorship：Coinvestigator(s)
Grant amount：\4420000 （ Direct Cost: \3400000 、 Indirect Cost：\1020000 ）
聴覚情報処理モデルをFPGA(Field Programmable Gate Array)にインプリメントし、本方式による聴覚情報処理のリアルタイム化を実現した。ピンクノイズ60db状態でSN比0dB程度でも音認識できることとなった。
Other External Funds
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近距離通信センサの受信距離拡張と位置情報推測技術の実現によるスマートフォンを活用した認知症高齢者見守り機構の研究開発
2015 - 2017
総務省 総務省
Mauricio Kugler、岩田 彰, 小竹 暢隆, 須藤 正時
Grant type：Competitive
BLEの利点である低消費電力を活かし，充電不要で使い捨てができる小型・軽量のセンサを実現するために，回折特性を向上し，電波の回り込み，伝搬距離を伸ばす．また，センサの電界強度から測定したセンサと受信機間の距離とスマートフォンの位置情報からセンサの位置を推定する測位技術を研究・開発する．本認知症高齢者見守り機構は，愛知県大府市，名古屋市中川区での社会実験を通して，社会での有用性を評価し，実用可能性を検証する．
Past of Cooperative Research
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Study of fatigue failure prediction by acoustic signals in die-cast molds
2020.08 - 2020.11
Meiwa Co. Ltd. Collaboration in Japan
Authorship：Coinvestigator(s)
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リハビリ併用型t-DCS装置の開発
2016.11 - 2019.10
オージー技研株式会社 Collaboration in Japan
高垣 俊之、田中 悟志
Authorship：Principal investigator
Teaching Experience
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2022.03 Institution：École Supérieure d'Ingénieurs en Génie Électrique
Level：Undergraduate (specialized) Country：France
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Embedded Digital Signal Processing
2021.11 - 2021.12 Institution：École Supérieure d'Ingénieurs en Génie Électrique
Level：Postgraduate Country：France
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2021.03 Institution：École Supérieure d'Ingénieurs en Génie Électrique
Level：Undergraduate (specialized) Country：France
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Embedded Digital Signal Processing
2020.11 - 2020.12 Institution：École Supérieure d'Ingénieurs en Génie Électrique
Level：Postgraduate Country：France
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2020.03 Institution：École Supérieure d'Ingénieurs en Génie Électrique
Level：Undergraduate (specialized) Country：France
Committee Memberships
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日本神経回路学会第22回全国大会実行委員会 実行委員
2011.09 - 2012.09
Committee type：Other