MAG
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Unravelling Complex Brain Networks with Automated 3-D Neural Mapping
-Automated 3-D brain imaging data analysis technology offers more reliable and standardized analysis of the spatial organization of complex neural circuits.-
KAIST researchers developed a new algorithm for brain imaging data analysis that enables the precise and quantitative mapping of complex neural circuits onto a standardized 3-D reference atlas.
Brain imaging data analysis is indispensable in the studies of neuroscience. However, analysis of obtained brain imaging data has been heavily dependent on manual processing, which cannot guarantee the accuracy, consistency, and reliability of the results.
Conventional brain imaging data analysis typically begins with finding a 2-D brain atlas image that is visually similar to the experimentally obtained brain image. Then, the region-of-interest (ROI) of the atlas image is matched manually with the obtained image, and the number of labeled neurons in the ROI is counted.
Such a visual matching process between experimentally obtained brain images and 2-D brain atlas images has been one of the major sources of error in brain imaging data analysis, as the process is highly subjective, sample-specific, and susceptible to human error. Manual analysis processes for brain images are also laborious, and thus studying the complete 3-D neuronal organization on a whole-brain scale is a formidable task.
To address these issues, a KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering developed new brain imaging data analysis software named 'AMaSiNe (Automated 3-D Mapping of Single Neurons)', and introduced the algorithm in the May 26 issue of Cell Reports.
AMaSiNe automatically detects the positions of single neurons from multiple brain images, and accurately maps all the data onto a common standard 3-D reference space. The algorithm allows the direct comparison of brain data from different animals by automatically matching similar features from the images, and computing the image similarity score.
This feature-based quantitative image-to-image comparison technology improves the accuracy, consistency, and reliability of analysis results using only a small number of brain slice image samples, and helps standardize brain imaging data analyses.
Unlike other existing brain imaging data analysis methods, AMaSiNe can also automatically find the alignment conditions from misaligned and distorted brain images, and draw an accurate ROI, without any cumbersome manual validation process.
AMaSiNe has been further proved to produce consistent results with brain slice images stained utilizing various methods including DAPI, Nissl, and autofluorescence.
The two co-lead authors of this study, Jun Ho Song and Woochul Choi, exploited these benefits of AMaSiNe to investigate the topographic organization of neurons that project to the primary visual area (VISp) in various ROIs, such as the dorsal lateral geniculate nucleus (LGd), which could hardly be addressed without proper calibration and standardization of the brain slice image samples.
In collaboration with Professor Seung-Hee Lee's group of the Department of Biological Science, the researchers successfully observed the 3-D topographic neural projections to the VISp from LGd, and also demonstrated that these projections could not be observed when the slicing angle was not properly corrected by AMaSiNe. The results suggest that the precise correction of a slicing angle is essential for the investigation of complex and important brain structures.
AMaSiNe is widely applicable in the studies of various brain regions and other experimental conditions. For example, in the research team’s previous study jointly conducted with Professor Yang Dan’s group at UC Berkeley, the algorithm enabled the accurate analysis of the neuronal subsets in the substantia nigra and their projections to the whole brain. Their findings were published in Science on January 24.
AMaSiNe is of great interest to many neuroscientists in Korea and abroad, and is being actively used by a number of other research groups at KAIST, MIT, Harvard, Caltech, and UC San Diego.
Professor Paik said, “Our new algorithm allows the spatial organization of complex neural circuits to be found in a standardized 3-D reference atlas on a whole-brain scale. This will bring brain imaging data analysis to a new level.”
He continued, “More in-depth insights for understanding the function of brain circuits can be achieved by facilitating more reliable and standardized analysis of the spatial organization of neural circuits in various regions of the brain.”
This work was supported by KAIST and the National Research Foundation of Korea (NRF).
Figure and Image Credit: Professor Se-Bum Paik, KAIST
Figure and Image Usage Restrictions: News organizations may use or redistribute these figures and images, with proper attribution, as part of news coverage of this paper only.
Publication:
Song, J. H., et al. (2020). Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex. Cell Reports. Volume 31, 107682. Available online at https://doi.org/10.1016/j.celrep.2020.107682
Profile:
Se-Bum Paik
Assistant Professor
sbpaik@kaist.ac.kr
http://vs.kaist.ac.kr/
VSNN Laboratory
Department of Bio and Brain Engineering
Program of Brain and Cognitive Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2020.06.08 View 14608 -
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster.
The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics.
Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain.
In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community.
During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence.
Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women.
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2020.05.26 View 13131 -
Highly Efficient Charge-to-Spin Interconversion in Graphene Heterostructures
Researchers present a new route for designing a graphene-based active spintronic component
KAIST physicists described a route to design the energy-efficient generation, manipulation and detection of spin currents using nonmagnetic two-dimensional materials. The research team, led by Professor Sungjae Cho, observed highly efficient charge-to-spin interconversion via the gate-tunable Rashba-Edelstien effect (REE) in graphene heterostructures.
This research paves the way for the application of graphene as an active spintronic component for generating, controlling, and detecting spin current without ferromagnetic electrodes or magnetic fields.
Graphene is a promising spintronic component owing to its long spin diffusion length. However, its small spin-orbit coupling limits the potential of graphene in spintronic applications since graphene cannot be used to generate, control, or detect spin current.
“We successfully increased the spin-orbit coupling of graphene by stacking graphene on top of 2H-TaS2, which is one of the transition metal dichalcogenide materials with the largest spin-orbit coupling. Graphene now can be used to generate, control, and detect spin current,” Professor Cho said.
The Rashba-Edelstein effect is a physical mechanism that enables charge current-to-spin current interconversion by spin-dependent band structure induced by the Rashba effect, a momentum-dependent splitting of spin bands in low-dimensional condensed matter systems.
Professor Cho’s group demonstrated the gate-tunable Rashba-Edelstein effect in a multilayer graphene for the first time. The Rahsba-Edelstein effect allows the two-dimensional conduction electrons of graphene to be magnetized by an applied charge current and form a spin current. Furthermore, as the Fermi level of graphene, tuned by gate voltage, moves from the valence to conduction band, the spin current generated by graphene reversed its spin direction.
This spin reversal is useful in the design of low-power-consumption transistors utilizing spins in that it provides the carrier “On” state with spin up holes (or spin down electrons) and the "Off" state with zero net spin polarization at so called “charge neutrality point” where numbers of electrons and holes are equal.
“Our work is the first demonstration of charge-to-spin interconversion in a metallic TMD (transition-metal dichalcogenides) and graphene heterostructure with a spin polarization state controlled by a gate. We expect that the all-electrical spin-switching effect and the reversal of non-equilibrium spin polarization by the application of gate voltage is applicable for the energy-efficient generation and manipulation of spin currents using nonmagnetic van der Waals materials,” explained Professor Cho.
This study (https://pubs.acs.org/doi/10.1021/acsnano.0c01037) was supported by the National Research Foundation of Korea.
Publication:
Lijun Li, Jin Zhang, Gyuho Myeong, Wongil Shin, Hongsik Lim, Boram Kim, Seungho Kim, Taehyeok Jin, Stuart Cavill, Beom Seo Kim, Changyoung Kim, Johannes Lischner, Aires Ferreira, and Sungjae Cho, Gate-Tunable Reversible Rashba−Edelstein Effect in a Few-Layer Graphene/2H-TaS2 Heterostructure at Room Temperature. ACS Nano 2020. Link to download the paper: https://pubs.acs.org/doi/10.1021/acsnano.0c01037
Profile:
Professor Sungjae Cho, PhD
sungjae.cho@kaist.ac.kr
http://qtak.kaist.ac.kr
Department of Physics
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Korea
2020.05.18 View 10708 -
Ultrathin but Fully Packaged High-Resolution Camera
- Biologically inspired ultrathin arrayed camera captures super-resolution images. -
The unique structures of biological vision systems in nature inspired scientists to design ultracompact imaging systems. A research group led by Professor Ki-Hun Jeong have made an ultracompact camera that captures high-contrast and high-resolution images. Fully packaged with micro-optical elements such as inverted micro-lenses, multilayered pinhole arrays, and gap spacers on the image sensor, the camera boasts a total track length of 740 μm and a field of view of 73°.
Inspired by the eye structures of the paper wasp species Xenos peckii, the research team completely suppressed optical noise between micro-lenses while reducing camera thickness. The camera has successfully demonstrated high-contrast clear array images acquired from tiny micro lenses. To further enhance the image quality of the captured image, the team combined the arrayed images into one image through super-resolution imaging.
An insect’s compound eye has superior visual characteristics, such as a wide viewing angle, high motion sensitivity, and a large depth of field while maintaining a small volume of visual structure with a small focal length. Among them, the eyes of Xenos peckii and an endoparasite found on paper wasps have hundreds of photoreceptors in a single lens unlike conventional compound eyes. In particular, the eye structures of an adult Xenos peckii exhibit hundreds of photoreceptors on an individual eyelet and offer engineering inspiration for ultrathin cameras or imaging applications because they have higher visual acuity than other compound eyes.
For instance, Xenos peckii’s eye-inspired cameras provide a 50 times higher spatial resolution than those based on arthropod eyes. In addition, the effective image resolution of the Xenos peckii’s eye can be further improved using the image overlaps between neighboring eyelets. This unique structure offers higher visual resolution than other insect eyes.
The team achieved high-contrast and super-resolution imaging through a novel arrayed design of micro-optical elements comprising multilayered aperture arrays and inverted micro-lens arrays directly stacked over an image sensor. This optical component was integrated with a complementary metal oxide semiconductor image sensor.
This is first demonstration of super-resolution imaging which acquires a single integrated image with high contrast and high resolving power reconstructed from high-contrast array images. It is expected that this ultrathin arrayed camera can be applied for further developing mobile devices, advanced surveillance vehicles, and endoscopes.
Professor Jeong said, “This research has led to technological advances in imaging technology. We will continue to strive to make significant impacts on multidisciplinary research projects in the fields of microtechnology and nanotechnology, seeking inspiration from natural photonic structures.”
This work was featured in Light Science & Applications last month and was supported by the National Research Foundation (NRF) of and the Ministry of Health and Welfare (MOHW) of Korea.
Image credit: Professor Ki-Hun Jeong, KAIST
Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only.
Publication:
Kisoo Kim, Kyung-Won Jang, Jae-Kwan Ryu, and Ki-Hun Jeong. (2020) “Biologically inspired ultrathin arrayed camera for high-contrast and high-resolution imaging”. Light Science & Applications. Volume 9. Article 28. Available online at https://doi.org/10.1038/s41377-020-0261-8
Profile:
Ki-Hun Jeong
Professor
kjeong@kaist.ac.kr
http://biophotonics.kaist.ac.kr/
Department of Bio and Brain Engineering
KAIST
Profile:
Kisoo Kim
Ph.D. Candidate
kisoo.kim1@kaist.ac.kr
http://biophotonics.kaist.ac.kr/
Department of Bio and Brain Engineering
KAIST
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2020.03.23 View 19472 -
Professor Jong Chul Ye Appointed as Distinguished Lecturer of IEEE EMBS
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was appointed as a distinguished lecturer by the International Association of Electrical and Electronic Engineers (IEEE) Engineering in Medicine and Biology Society (EMBS). Professor Ye was invited to deliver a lecture on his leading research on artificial intelligence (AI) technology in medical video restoration. He will serve a term of two years beginning in 2020.
IEEE EMBS's distinguished lecturer program is designed to educate researchers around the world on the latest trends and technology in biomedical engineering. Sponsored by IEEE, its members can attend lectures on the distinguished professor's research subject.
Professor Ye said, "We are at a time where the importance of AI in medical imaging is increasing.” He added, “I am proud to be appointed as a distinguished lecturer of the IEEE EMBS in recognition of my contributions to this field.”
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2020.02.27 View 11883 -
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging.
As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE.
He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA.
Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
2019.12.18 View 10886 -
Gallium-Based Solvating Agent Efficiently Analyzes Optically Active Alcohols
A KAIST research team has developed a gallium-based metal complex enabling the rapid chiral analysis of alcohols. A team working under Professor Hyunwoo Kim reported the efficient new alcohol analysis method using nuclear magnetic resonance (NMR) spectroscopy in iScience.
Enantiopure chiral alcohols are ubiquitous in nature and widely utilized as pharmaceuticals. This importance of chirality in synthetic and medicinal chemistry has advanced the search for rapid and facile methods to determine the enantiomeric purities of compounds. To date, chiral analysis has been performed using high-performance liquid chromatography (HPLC) with chiral columns.
Along with the HPLC technique, chiral analysis using NMR spectroscopy has gained tremendous attention as an alternative to traditionally employed chromatographic methods due to its simplicity and rapid detection for real-time measurement. However, this method carries drawbacks such as line-broadening, narrow substrate scope, and poor resolution. Thus, compared with popular methods of chromatographic analysis, NMR spectroscopy is infrequently used for chiral analysis.
In principle, a chiral solvating agent is additionally required for the NMR measurement of chiral alcohols to obtain two distinct signals. However, NMR analysis of chiral alcohols has been challenging due to weak binding interactions with chiral solvating agents. To overcome the intrinsic difficulty of relatively weak molecular interactions that are common for alcohols, many researchers have used multifunctional alcohols to enhance interactions with solvating agents.
Instead, the KAIST team successfully varied the physical properties of metal complexes to induce stronger interactions with alcohols rather than the strategy of using multifunctional analytes, in the hopes of developing a universal chiral solvating agent for alcohols. Compared to the current method of chiral analysis used in the pharmaceutical industry, alcohols that do not possess chromophores can also be directly analyzed with the gallium complexes.
Professor Kim said that this method could be a complementary chiral analysis technique at the industry level in the near future. He added that since the developed gallium complex can determine enantiomeric excess within minutes, it can be further utilized to monitor asymmetric synthesis. This feature will benefit a large number of researchers in the organic chemistry community, as well as the pharmaceutical industry.
(Figure: Schematic view of the in-situ direct 1H NMR chiral analysis.)
-Profile:
Professor Hyunwoo Kim
Department of Chemistry
KAIST
http://mdos.kaist.ac.kr
hwk34@kaist.ac.kr
For more on this article,
please go to https://doi.org/10.1016/j.isci2019.07051
2019.11.14 View 11829 -
Image Analysis to Automatically Quantify Gender Bias in Movies
Many commercial films worldwide continue to express womanhood in a stereotypical manner, a recent study using image analysis showed. A KAIST research team developed a novel image analysis method for automatically quantifying the degree of gender bias in films.
The ‘Bechdel Test’ has been the most representative and general method of evaluating gender bias in films. This test indicates the degree of gender bias in a film by measuring how active the presence of women is in a film. A film passes the Bechdel Test if the film (1) has at least two female characters, (2) who talk to each other, and (3) their conversation is not related to the male characters.
However, the Bechdel Test has fundamental limitations regarding the accuracy and practicality of the evaluation. Firstly, the Bechdel Test requires considerable human resources, as it is performed subjectively by a person. More importantly, the Bechdel Test analyzes only a single aspect of the film, the dialogues between characters in the script, and provides only a dichotomous result of passing the test, neglecting the fact that a film is a visual art form reflecting multi-layered and complicated gender bias phenomena. It is also difficult to fully represent today’s various discourse on gender bias, which is much more diverse than in 1985 when the Bechdel Test was first presented.
Inspired by these limitations, a KAIST research team led by Professor Byungjoo Lee from the Graduate School of Culture Technology proposed an advanced system that uses computer vision technology to automatically analyzes the visual information of each frame of the film. This allows the system to more accurately and practically evaluate the degree to which female and male characters are discriminatingly depicted in a film in quantitative terms, and further enables the revealing of gender bias that conventional analysis methods could not yet detect.
Professor Lee and his researchers Ji Yoon Jang and Sangyoon Lee analyzed 40 films from Hollywood and South Korea released between 2017 and 2018. They downsampled the films from 24 to 3 frames per second, and used Microsoft’s Face API facial recognition technology and object detection technology YOLO9000 to verify the details of the characters and their surrounding objects in the scenes.
Using the new system, the team computed eight quantitative indices that describe the representation of a particular gender in the films. They are: emotional diversity, spatial staticity, spatial occupancy, temporal occupancy, mean age, intellectual image, emphasis on appearance, and type and frequency of surrounding objects.
Figure 1. System Diagram
Figure 2. 40 Hollywood and Korean Films Analyzed in the Study
According to the emotional diversity index, the depicted women were found to be more prone to expressing passive emotions, such as sadness, fear, and surprise. In contrast, male characters in the same films were more likely to demonstrate active emotions, such as anger and hatred.
Figure 3. Difference in Emotional Diversity between Female and Male Characters
The type and frequency of surrounding objects index revealed that female characters and automobiles were tracked together only 55.7 % as much as that of male characters, while they were more likely to appear with furniture and in a household, with 123.9% probability.
In cases of temporal occupancy and mean age, female characters appeared less frequently in films than males at the rate of 56%, and were on average younger in 79.1% of the cases. These two indices were especially conspicuous in Korean films.
Professor Lee said, “Our research confirmed that many commercial films depict women from a stereotypical perspective. I hope this result promotes public awareness of the importance of taking prudence when filmmakers create characters in films.”
This study was supported by KAIST College of Liberal Arts and Convergence Science as part of the Venture Research Program for Master’s and PhD Students, and will be presented at the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) on November 11 to be held in Austin, Texas.
Publication:
Ji Yoon Jang, Sangyoon Lee, and Byungjoo Lee. 2019. Quantification of Gender Representation Bias in Commercial Films based on Image Analysis. In Proceedings of the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW). ACM, New York, NY, USA, Article 198, 29 pages. https://doi.org/10.1145/3359300
Link to download the full-text paper:
https://files.cargocollective.com/611692/cscw198-jangA--1-.pdf
Profile: Prof. Byungjoo Lee, MD, PhD
byungjoo.lee@kaist.ac.kr
http://kiml.org/
Assistant Professor
Graduate School of Culture Technology (CT)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr Daejeon 34141, Korea
Profile: Ji Yoon Jang, M.S.
yoone3422@kaist.ac.kr
Interactive Media Lab
Graduate School of Culture Technology (CT)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr Daejeon 34141, Korea
Profile: Sangyoon Lee, M.S. Candidate
sl2820@kaist.ac.kr
Interactive Media Lab
Graduate School of Culture Technology (CT)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr Daejeon 34141, Korea
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2019.10.17 View 26730 -
Professor Byong-Guk Park Named Scientist of October
< Professor Byong-Guk Park >
Professor Byong-Guk Park from the Department of Materials Science and Engineering was selected as the ‘Scientist of the Month’ for October 2019 by the Ministry of Science and ICT and the National Research Foundation of Korea. Professor Park was recognized for his contributions to the advancement of spin-orbit torque (SOT)-based magnetic random access memory (MRAM) technology. He received 10 million KRW in prize money.
A next-generation, non-volatile memory device MRAM consists of thin magnetic films. It can be applied in “logic-in-memory” devices, in which logic and memory functionalities coexist, thus drastically improving the performance of complementary metal-oxide semiconductor (CMOS) processors. Conventional MRAM technology is limited in its ability to increase the operation speed of a memory device while maintaining a high density.
Professor Park tackled this challenge by introducing a new material, antiferromagnet (IrMn), that generates a sizable amount of SOT as well as an exchange-bias field, which makes successful data writing possible without an external magnetic field. This research outcome paved the way for the development of MRAM, which has a simple device structure but features high speeds and density.
Professor Park said, “I feel rewarded to have forwarded the feasibility and applicability of MRAM. I will continue devoting myself to studying further on the development of new materials that can help enhance the performance of memory devices."
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2019.10.10 View 10928 -
KAIST Team Reaching Out with Appropriate Technology
(The gold prize winning team of KATT)
The KAIST Appropriate Technology Team (KATT) consisting of international students at KAIST won the gold and silver prizes at ‘The 10th Creative Design Competition for the Other 90 Percent.’
More than 218 students from 50 teams nationwide participated in the competition hosted by the Ministry of Science and ICT last month.
The competition was created to discover appropriate technology and sustainable design items to enhance the quality of life for those with no or few accessible technologies.
A team led by Juan Luis Gonzalez Bello, graduate student from the School of Electrical Engineering received the gold prize for presenting a prosthetic arm. Their artificial arm was highly recognized for its affordability and good manageability. The team said that it cost less than 10 US dollars to construct from materials available in underprivileged regions and was easy to assemble.
Sophomore Hutomo Calvin from the Department of Materials Science & Engineering also worked on the prosthetic arm project with freshmen Bella Godiva, Stephanie Tan, and Koptieuov Yearbola.
Alexandra Tran, senior from the School of Electrical Engineering led the silver prize winning team. Her team developed a portable weather monitor, ‘Breathe Easy’. She worked with Alisher Tortay, senior from the School of Computing, Ashar Alam, senior from the Department of Mechanical Engineering, Bereket Eshete, junior from the School of Computing, and Marthens Hakzimana, sophomore from the Department of Mechanical Engineering.
This weather monitor is a low-cost but efficient air quality monitor. The team said it just cost less than seven US dollars to construct the monitor.KAIST students have now won the gold prize for two consecutive years.
2018.06.19 View 12353 -
Professor YongKeun Park Wins the 2018 Fumio Okano Award
(Professor Park)
Professor YongKeun Park from the Department of Physics won the 2018 Fumio Okano Award in recognition of his contributions to 3D display technology development during the annual conference of the International Society for Optics and Photonics (SPIE) held last month in Orlando, Florida in the US.
The Fumio Okano Best 3D Paper Prize is presented annually in memory of Dr. Fumio Okano, a pioneer and innovator of 3D displays who passed away in 2013, for his contributions to the field of 3D TVs and displays. The award is sponsored by NHK-ES.
Professor Park and his team are developing novel technology for measuring and visualizing 3D images by applying random light scattering. He has published numerous papers on 3D holographic camera technology and 3000x enhanced performance of 3D holographic displays in renowned international journals such as Nature Photonics, Nature Communications, and Science Advances. His technology has drawn international attention from renowned media outlets including Newsweek and Forbes.
He has established two startups to commercialize his technology. Tomocube specializes in 3D imaging microscopes using holotomographic technology and the company exports their products to several countries including the US and Japan. The.Wave.Talk is exploring technology for examining pre-existing bacteria anywhere and anytime.
Professor Park’s innovations have already been recognized in and out of KAIST. In February, he was selected as the KAISTian of the Year for his outstanding research, commercialization, and startups. He was also decorated with the National Science Award in April by the Ministry of Science and ICT and the Hong Jin-Ki Innovation Award later in May by the Yumin Cultural Foundation.
Professor Park said, “3D holography is emerging as a significant technology with growing potential and positive impacts on our daily lives. However, the current technology lags far behind the levels displayed in SF movies. We will do our utmost to reach this level with more commercialization."
2018.05.31 View 11972 -
Seong-Tae Kim Wins Robert-Wagner All-Conference Best Paper Award
(Ph.D. candidate Seong-Tae Kim)
Ph.D. candidate Seong-Tae Kim from the School of Electrical Engineering won the Robert Wagner All-Conference Best Student Paper Award during the 2018 International Society for Optics and Photonics (SPIE) Medical Imaging Conference, which was held in Houston last month.
Kim, supervised by Professor Yong Man Ro, received the award for his paper in the category of computer-aided diagnosis. His paper, titled “ICADx: Interpretable Computer-Aided Diagnosis of Breast Masses”, was selected as the best paper out of 900 submissions. The conference selects the best paper in nine different categories. His research provides new insights on diagnostic technology to detect breast cancer powered by deep learning.
2018.03.15 View 11617