KUGLER Mauricio

写真a

Affiliation Department etc.

Department of Computer Science
Department of Computer Science

Title

Assistant Professor

Graduating School

  •  
    -
    2001.02

    Federal Technological University of Parana   Faculty of Engineering   Department of Electronics   Graduated

Graduate School

  •  
    -
    2007.03

    Nagoya Institute of Technology  Graduate School, Division of Information Engineering  Doctor's Course  Completed

  •  
    -
    2003.02

    Federal Technological University of Paraná  Graduate School, Division of Engineering  Master's Course  Completed

Degree

  • Nagoya Institute of Technology -  Doctor of Philosophy

External Career

  • 2014.03
    -
    2014.04

    ESIGELEC   Graduate School of Engineering   Lecturer  

  • 2013.11
     
     

    ESIGELEC   Graduate School of Engineering   Lecturer  

  • 2013.05
     
     

    ESIGELEC   Graduate School of Engineering   Lecturer  

  • 2012.11
    -
    2012.12

    ESIGELEC   Graduate School of Engineering   Lecturer  

  • 2012.05
     
     

    ESIGELEC   Gradiate School of Engineering   Lecturer  

Academic Society Affiliations

  • 2005.01
    -
    2014.04

    Institute of Electrical and Electronics Engineers (IEEE)

 

Papers

  • Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation

    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 ( Springer )  14 ( 12 ) 2047 - 2055   2019.07  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

    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.

  • Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains

    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  [Refereed]

    Research paper (international conference proceedings)   Multiple Authorship

    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.

  • Spatiotemporal information integration model of pancreatic cancer and human embryo

    Hidekata Hontani, Mauricio Kugler, Akinobu Shimizu

    The CELL   50 ( 1 ) 5 - 8   2018.01  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Design of a compact sound localization device on a stand-alone FPGA-based platform

    Mauricio Kugler, Teemu Tossavainen, Susumu Kuroyanagi, Akira Iwata

    電子情報通信学会論文誌 ( 電子情報通信学会 )  E99-D ( 11 ) 2682 - 2693   2016.11  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

    Sound localization systems are widely studied and have several potential applications, including hearing aid devices, surveillance and robotics. However, few proposed solutions target portable systems, such as wearable devices, which require a small unnoticeable platform, or unmanned aerial vehicles, in which weight and low power consumption are critical aspects. The main objective of this research is to achieve real-time sound localization capability in a small, self-contained device, without having to rely on large shaped platforms or complex microphone arrays. The proposed device has two surface-mount microphones spaced only 20 mm apart. Such reduced dimensions present challenges for the implementation, as differences in level and spectra become negligible, and only time-difference of arrival (TDoA) can be used as a localization cue. Three main issues have to be addressed in order to accomplish these objectives. To achieve real-time processing, the TDoA is calculated using zero-crossing spikes applied to the hardware-friendly Jeffers model. In order to make up for the reduction in resolution due to the small dimensions, the signal is upsampled several-fold within the system. Finally, a coherence-based spectral masking is used to select only frequency components with relevant TDoA information. The proposed system was implemented on a field-programmable gate array (FPGA) based platform, due to the large amount of concurrent and independent tasks, which can be efficiently parallelized in reconfigurable hardware devices. Experimental results with white-noise and environmental sounds show high accuracies for both anechoic and reverberant conditions.

  • Real-time hardware implementation of a sound recognition system with in-field learning

    Mauricio Kugler, Teemu Tossavainen, Miku Nakatsu, Susumu Kuroyanagi, Akira Iwata

    電子情報通信学会論文誌 ( 電子情報通信学会 )  E99-D ( 7 ) 1885 - 1894   2016.07  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

    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.

  • Combined Methodology for Linear Time Series Forecasting

    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) ( 一般社団法人 電気学会 )  15 ( 12 ) 1780 - 1790   2020.10  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

    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.

  • Partial rigid diffeomorphism for measuring temporal change of pancreatic cancer tumor

    Yuki Tamura, Tatsuya Yokota, Mauricio Kugler, Valentin Triquet, Tomonari Sei, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani

    Proceedings of the International Forum on Medical Imaging in Asia IFMIA2019     2019.01  [Refereed]

    Research paper (international conference proceedings)   Single Author

  • Construction of multimodal 3D model of pancreatic cancer tumor

    Yushi Goto, Mauricio Kugler, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani

    Proceedings of the International Forum on Medical Imaging in Asia IFMIA2019     2019.01  [Refereed]

    Research paper (international conference proceedings)   Multiple Authorship

    Histopathological imaging and Magnetic Resonance (MR) are two equally important yet very distinct modalities of medical imaging. The high resolution of the first and the non-invasiveness of the later provide complementary information for medical diagnosis and research. Due to their largely different resolutions, the registration between 3D images of these two modalities is challenging. The objective of this paper is to create a multimodal 3D model of pancreatic cancer tumor by performing the registration of a reconstructed 3D pathological image and an MR image from a KPC mouse. The tumor portions were manually segmented and the 3D pathological image was reconstructed using landmark-based non-linear registration. The process starts by registering the outline of the images using the LDDMM non-linear registration method to match the binary labels of the tumor regions. Next, a non-linear B-spline deformation method based on mutual information maximization is used to register the internal structures of the images. Experimental results show that the overall shape of the tumor and its internal necrosis portion could be correctly registered, although the quality of the manual segmentations affects the accuracy of the registration.

  • Registration between histopathological images with different stains and an MRI Image of Pancreatic Cancer Tumor

    Hidekata Hontani, Yushi Goto, Yuki Tamura, Tomoshige Shimomura, Naoki Kawamura, Hirokazu Kobayashi, Mauricio Kugler, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Takahiro Katagiri, Tomonari Sei, Akinobu Shimizu

    Proceedings of the International Forum on Medical Imaging in Asia IFMIA2019     2019.01  [Refereed]

    Research paper (international conference proceedings)   Multiple Authorship

    In this paper, we report on the construction of a pancreatic tumor model that represents the relationship between the tumor growth and the micro anatomical structures. The former, the tumor growth, is described by referring to the temporal series of MRI images of the whole body and the latter, the micro structures of the tumor, is described by a spatial series of microscopic images of thin-sections sliced from the extracted pancreatic tumor. For the model construction, we developed new non-rigid registration methods for (1) accurate description of tumor growth, (2) reconstruction of 3D microscopic images, and (3) registration between an MRI image and corresponding microscopic images. In addition, we constructed a neural network that can generate a set of fake microscopic image patches of a pancreatic tumor that corresponds to each voxel inside the tumor region in an MRI image. The outlines of the methods are introduced and some examples of experimental results are demonstrated.

  • Construction of a Multi-modal Model of Pancreatic Tumors by Integration of MRI and Pathological Images using Conditional Cycle alpha-GAN

    Tomoshige Shimomura, Mauricio Kugler, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani

    Proceedings of the 1st Workshop on Multi-Discipline Approach for Learning Concepts - International Conference on Computer Vision     2019  [Refereed]

    Research paper (international conference proceedings)   Multiple Authorship

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Presentations

  • Construction of a Multi-modal Model of Pancreatic Tumors by Integration of MRI and Pathological Images using Conditional Cycle alpha-GAN

    Tomoshige Shimomura, Mauricio Kugler, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani

    ICCV Workshop for Multi-Discipline Approach for Learning Concepts (MDALC)  2019.10  -  2019.11 

  • Construction of multimodal 3D model of pancreatic cancer tumor

    Yushi Goto, Mauricio Kugler

    International Forum on Medical Imaging in Asia - IFMIA2019  (Nanyang Technological University)  2019.01  -  2019.01 

  • Partial rigid diffeomorphism for measuring temporal change of pancreatic cancer tumor

    Yuki Tamura, Mauricio Kugler

    International Forum on Medical Imaging in Asia - IFMIA2019  (Nanyang Technological University)  2019.01  -  2019.01 

  • Registration between histopathological images with different stains and an MRI Image of Pancreatic Cancer Tumor

    Hidekata Hontani, Yushi Goto, Yuki Tamura, Tomoshige Shimomura, Naoki Kawamura, Hirokazu Kobayashi, Mauricio Kugler, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Takahiro Katagiri, Tomonari Sei, Akinobu Shimizu

    International Forum on Medical Imaging in Asia - IFMIA2019  2019.01  -  2019.01 

  • Workshop in Computational Pathology - Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains

    Mauricio Kugler

    21st International Conference on Medical Image Computing & Computer Assisted Intervention  (Granada, Spain)  2018.09  -  2018.09 

  • Workshop in Computational Pathology - Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image

    Tomoshige Shimomura, Mauricio Kugler

    21st International Conference on Medical Image Computing & Computer Assisted Intervention  (Granada, Spain)  2018.09  -  2018.09 

  • Construction of multi-scale spatiotemporal model of pancreas tumor from pathology images and time series MR images

    TRIQUET, V., KUGLER, M., KOBAYASHI, H., YOKOTA, T., IWAMOTO, C., OUCHIDA, K., HASHIZUME, M., HONTANI, H.

    31st International Congress on Computer Assisted Radiology and Surgery  (Barcelona, Spain)  2017.06  -  2017.06 

  • The Impact of Memory Dependency on Precision Forecast - An Analysis on Different Types of Time Series Databases

    Ricardo Moraes Muniz da Silva

    6th International Conference on Pattern Recognition Application and Methods  (Porto, Portugal)  2017.02  -  2017.02 

  • Design and Performance Evaluation of Wearable BLE Antenna for a Localization System of Aged Wanderer

    Yuto Shimizu, Shun Hiyama, Daisuke Anzai, Mauricio Kugler, Akira Iwata, Jianqing Wang

    IEEE International Conference on Biomedical and Health Informatics  2017.02  -  2017.02 

  • Wearable sound localization assistive device for the hearing impaired

    Mauricio Kugler

    25th Brazilian Congress on Biomedical Engineering  (Foz do Iguaçu, Brazil)  2016.10  -  2016.10  Brazilian Society of Biomedical Engineering

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Industrial Property

Other research activities

  • Development of a Wearable Brain Stimulation Device for Gait Rehabilitation

    2013.04  -  2015.03

  • Sound recognition & visualization using a head mount display

    2013.04  -  2014.03

  • Sound Watcher: hardware and software development for a consumer product.

    2011.01  -  Now

Grant-in-Aid for Scientific Research

  • Wearable Brain Stimulation Device for Gait Rehabilitation

    Grant-in-Aid for challenging Exploratory Research

    Project Year: 2013.04  -  2015.03  Investigator(s): Satoshi Tanaka

    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.

  • Visualization of sound using head-mounted display

    Grant-in-Aid for Scientific Research(C)

    Project Year: 2011.04  -  2014.03  Investigator(s): Masatoki Suto

Joint Research activities

  • Study of fatigue failure prediction by acoustic signals in die-cast molds

    Offer organization: Meiwa Co. Ltd.   Collaboration in Japan  

    Project Year: 2020.08  -  2020.11