Ilwoo Lyu

(류일우)

3D Shape Analysis Lab (ShapeLab) Computer Science and Engineering Engineering Bldg. 106, 801-5 50 UNIST-gil, Ulsan 44919, Korea E-mail: ilwoolyu at unist ac kr Phone: +82-52-217-2298 Affiliations: @UNIST [CSE] [BME] @Vanderbilt [CS] [VISE] [Google Scholar] [dblp] [GitHub] orcid.org/0000-0001-5868-9603 Last Updated: November 2021

About Myself

Research Interests

• 3D Shape Analysis
• Image Processing
• Computer Vision
• Machine Learning
• Medical Image Analysis

Education

The University of North Carolina at Chapel Hill Doctor of Philosophy in Computer Science	2011 - 2017
Korea Advanced Institute of Science and Technology (KAIST) Master of Science in Computer Science	2009 - 2011
KAIST Bachelor of Science in Computer Science	2002 - 2009

Employment

UNIST, Ulsan, Korea Assistant Professor	Mar 2021 - Present
Vanderbilt University, Nashville, TN, USA Adjoint Assistant Professor	Mar 2021 - Present
MASI Lab, Vanderbilt University, Nashville, TN, USA Research Assistant Professor Mentor: Dr. Bennett Landman	Mar 2017 - Feb 2021
Neuro Image Research and Analysis Lab, UNC, Chapel Hill, NC, USA Research Assistant Advisor: Dr. Martin Styner	Dec 2011 - Feb 2017
IBM Almaden Reserach Center, San Jose, California, USA Intern Mentor: Dr. Tanveer F. Syeda-Mahmood and Dr. Colin B. Compas	May 2015 - Jul 2015
Computer Graphics Laboratory, KAIST, Daejeon, Korea Research Assistant Advisor: Dr. Sung Yong Shin and Dr. Joon-Kyung Seong	Jun 2010 - May 2011
Cognitive Robotic Vision Laboratory, KIST, Seoul, Korea Intern Mentor: Dr. Sung-Kee Park	Mar 2009 - May 2009

	Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training [DOI] [PMID:33497773] [Abstract] [Software] Ilwoo Lyu, Shunxing Bao, Lingyan Hao, Jewelia Yao, Jacob Miller, Willa Voorhies, Warren Taylor, Silvia Bunge, Kevin Weiner, Bennett Landman NeuroImage, 229, 117758, 2021
The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex. Hide
	Cortical Surface Parcellation using Spherical Convolutional Neural Networks [DOI] [PMID:31803864] [Abstract] [Software] Prasanna Parvathaneni, Shunxing Bao, Vishwesh Nath, Neil Woodward, Daniel Claassen, Carissa Cascio, David Zald, Yuankai Huo, Bennett Landman, Ilwoo Lyu Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019, LNCS11766, 501-509, Shenzhen, China, 2019, early accept, travel award
We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with slow processing speed on a single subject (2-3 h). Moreover, even optimal surface registration does not necessarily produce optimal cortical parcellation as parcel boundaries are not fully matched to the geometric features. In this context, a choice of training features is important for accurate cortical parcellation. To utilize the networks efficiently, we propose cortical parcellation-specific input data from an irregular and complicated structure of cortical surfaces. To this end, we align ground-truth cortical parcel boundaries and use their resulting deformation fields to generate new pairs of deformed geometric features and parcellation maps. To extend the capability of the networks, we then smoothly morph cortical geometric features and parcellation maps using the intermediate deformation fields. We validate our method on 427 adult brains for 49 labels. The experimental results show that our method outperforms traditional multi-atlas and naive spherical U-Net approaches, while achieving full cortical parcellation in less than a minute. Hide
	Hierarchical Spherical Deformation for Cortical Surface Registration [DOI] [PMID:31280090] [Abstract] [Software] Ilwoo Lyu, Hakmook Kang, Neil Woodward, Martin Styner, Bennett Landman Medical Image Analysis, 57, 72-88, 2019
We present hierarchical spherical deformation for a group-wise shape correspondence to address template selection bias and to minimize registration distortion. In this work, we aim at a continuous and smooth deformation field to guide accurate cortical surface registration. In conventional spherical registration methods, a global rigid alignment and local deformation are independently performed. Motivated by the composition of precession and intrinsic rotation, we simultaneously optimize global rigid rotation and non-rigid local deformation by utilizing spherical harmonics interpolation of local composite rotations in a single framework. To this end, we indirectly encode local displacements by such local composite rotations as functions of spherical locations. Furthermore, we introduce an additional regularization term to the spherical deformation, which maximizes its rigidity while reducing registration distortion. To improve surface registration performance, we employ the second order approximation of the energy function that enables fast convergence of the optimization. In the experiments, we validate our method on healthy normal subjects with manual cortical surface parcellation in registration accuracy and distortion. We show an improved shape correspondence with high accuracy in cortical surface parcellation and significantly low registration distortion in surface area and edge length. In addition to validation, we discuss parameter tuning, optimization, and implementation design with potential acceleration. Hide
	Hierarchical Spherical Deformation for Shape Correspondence [DOI] [PMID:31803863] [Abstract] [Software] Ilwoo Lyu, Martin Styner, Bennett Landman Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018, LNCS11070, 853-861, Granada, Spain, 2018, early accept, oral presentation
We present novel spherical deformation for a landmark-free shape correspondence in a group-wise manner. In this work, we aim at both addressing template selection bias and minimizing registration distortion in a single framework. The proposed spherical deformation yields a non-rigid deformation field without referring to any particular spherical coordinate system. Specifically, we extend a rigid rotation represented by well-known Euler angles to general non-rigid local deformation via spatial-varying Euler angles. The proposed method employs spherical harmonics interpolation of the local displacements to simultaneously solve rigid and non-rigid local deformation during the optimization. This consequently leads to a continuous, smooth, and hierarchical representation of the deformation field that minimizes registration distortion. In addition, the proposed method is group-wise registration that requires no specific template to establish a shape correspondence. In the experiments, we show an improved shape correspondence with high accuracy in cortical surface parcellation as well as significantly low registration distortion in surface area and edge length compared to the existing registration methods while achieving fast registration in 3 m per subject. Hide
	A Cortical Shape-Adaptive Approach to Local Gyrification Index [DOI] [PMID:29990689] [Abstract] [Software] Ilwoo Lyu, Sun Hyung Kim, Jessica Girault, John Gilmore, Martin Styner Medical Image Analysis, 48, 244-258, 2018
The amount of cortical folding, or gyrification, is typically measured within local cortical regions covered by an equidistant geodesic or nearest neighborhood-ring kernel. However, without careful design, such a kernel can easily cover multiple sulcal and gyral regions that may not be functionally related. Furthermore, this can result in smoothing out details of cortical folding, which consequently blurs local gyrification measurements. In this paper, we propose a novel kernel shape to locally quantify cortical gyrification within sulcal and gyral regions. We adapt wavefront propagation to generate a spatially varying kernel shape that encodes cortical folding patterns: neighboring gyral crowns, sulcal fundi, and sulcal banks. For this purpose, we perform anisotropic wavefront propagation that runs fast along gyral crowns and sulcal fundi by solving a static Hamilton-Jacobi partial differential equation. The resulting kernel adaptively elongates along gyral crowns and sulcal fundi, while keeping a uniform shape over flat regions like sulcal banks. We then measure local gyrification within the proposed spatially varying kernel. The experimental results show that the proposed kernel-based gyrification measure achieves a higher reproducibility than the conventional method in a multi-scan dataset. We further apply the proposed kernel to a brain development study in the early postnatal phase from neonate to 2 years of age. In this study we find that our kernel yields both positive and negative associations of gyrification with age, whereas the conventional method only captures positive associations. In general, our method yields sharper and more detailed statistical maps that associate cortical folding with sex and gestational age. Hide
	TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction [DOI] [PMID:29969416] [Abstract] [Software] Ilwoo Lyu, Sun Hyung Kim, Neil Woodward, Martin Styner, Bennett Landman IEEE Transactions on Medical Imaging, 37(7), 1653-1663, 2018
A proper geometric representation of the cortical regions is a fundamental task for cortical shape analysis and landmark extraction. However, a significant challenge has arisen due to the highly variable, convoluted cortical folding patterns. In this paper, we propose a novel topological graph representation for automatic sulcal curve extraction (TRACE). In practice, the reconstructed surface suffers from noise influences introduced during image acquisition/surface reconstruction. In the presence of noise on the surface, TRACE determines stable sulcal fundic regions by employing the line simplification method that prevents the sulcal folding pattern from being significantly smoothed out. The sulcal curves are then traced over the connected graph in the determined regions by the Dijkstra's shortest path algorithm. For validation, we used state-of-the-art surface reconstruction pipelines on a reproducibility dataset. The experimental results showed a higher reproducibility and robustness to noise in TRACE than the existing method (Li et al. 2010) with over 20% relative improvement in error for both surface reconstruction pipelines. In addition, the extracted sulcal curves by TRACE were well-aligned with manually delineated primary sulcal curves. We also provided a choice of parameters to control a quality of the extracted sulcal curves and showed the influences of the parameter selection on the resulting curves. Hide
	Novel Local Shape-Adaptive Gyrification Index with Application to Brain Development [DOI] [Abstract] [Software] Ilwoo Lyu, Sun Hyung Kim, Jessica Bullins, John Gilmore, Martin Styner Medical Image Computing and Computer Assisted Intervention (MICCAI) 2017, LNCS10433, 31-39, Quebec City, Quebec, Canada, 2017
Conventional approaches to quantification of the cortical folding employ a simple circular kernel. Such a kernel commonly covers multiple cortical gyral/sulcal regions that may be functionally unrelated and also often blurs local gyrification measurements. We propose a novel adaptive kernel for quantification of the local cortical folding, which incorporates neighboring gyral crowns and sulcal fundi. The proposed kernel is adaptively elongated to cover regions along the cortical folding patterns. The experimental results showed that the proposed kernel-based gyrification measure achieved a higher reproducibility in a multi-scan human phantom dataset and captured the cortical folding in a more shape-adaptive way than the conventional method. In early human brain development, we found positive correlations with age over most cortical regions as previously found as well as novel, refined regions of both positive and negative correlations undetectable by the conventional method. Hide
	Group-Wise Cortical Correspondence via Sulcal Curve-Constrained Entropy Minimization [DOI] [PMID:24683983] [Abstract] [Tutorial] Ilwoo Lyu, Sun Hyung Kim, Joon-Kyung Seong, Sang Wook Yoo, Alan Evans, Yundi Shi, Mar Sanchez, Marc Niethammer, Martin Styner Information Processing in Medical Imaging (IPMI) 2013, LNCS7917, 364-375, Asilomar, California, USA, 2013, oral presentation with open-ended discussion
We present a novel cortical correspondence method employing group-wise registration in a spherical parametrization space for the use in local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is unbiased registration that estimates a continuous smooth deformation field into an unbiased average space via sulcal curve-constrained entropy minimization using spherical harmonic decomposition of the spherical deformation field. We initialize a correspondence by our pair-wise method that establishes a surface correspondence with a prior template. Since this pair-wise correspondence is biased to the choice of a template, we further improve the correspondence by employing unbiased ensemble entropy minimization across all surfaces, which yields a deformation field onto the iteratively updated unbiased average. The specific entropy metric incorporates two terms: the first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth maps. We also propose an encoding scheme for spherical deformation via spherical harmonics as well as a novel method to choose an optimal spherical polar coordinate system for the most efficient deformation field estimation. The experimental results show evidence that the proposed method improves the correspondence quality in non-human primate and human subjects as compared to the pair-wise method. Hide
	Spectral-based Automatic Labeling and Refining of Human Cortical Sulcal Curves using Expert-Provided Examples [DOI] [PMID:20363334] [Abstract] Ilwoo Lyu, Joon-Kyung Seong, Sung Yong Shin, Kiho Im, Jee Hoon Roh, Min-Jeong Kim, Geon Ha Kim, Jong Hun Kim, Alan Evans, Duk L. Na, Jong-Min Lee NeuroImage, 52(1), 142-157, 2010
We present a spectral-based method for automatically labeling and refining major sulcal curves of a human cerebral cortex. Given a set of input (unlabeled) sulcal curves automatically extracted from a cortical surface and a collection of expert-provided examples (labeled sulcal curves), our objective is to identify the input major sulcal curves and assign their neuroanatomical labels, and then refines these curves based on the expert-provided example data, without employing any atlas-based registration scheme as preprocessing. In order to construct the example data, neuroanatomists manually labeled a set of 24 major sulcal curves (12 each for the left and right hemispheres) for each individual subject according to a precise protocol. We collected 30 sets of such curves from 30 subjects. Given the raw input sulcal curve set of a subject, we choose the most similar example curve to each input curve in the set to label and refine the latter according to the former. We adapt a spectral matching algorithm to choose the example curve by exploiting the sulcal curve features and their relationship. The high dimensionality of sulcal curve data in spectral matching is addressed by using their multi-resolution representations, which greatly reduces time and space complexities. Our method provides consistent labeling and refining results even under high variability of cortical sulci across the subjects. Through experiments we show that the results are comparable in accuracy to those done manually. Most output curves exhibited accuracy values higher than 80%, and the mean accuracy values of the curves in the left and the right hemispheres were 84.69% and 84.58%, respectively. Hide

Teaching

Fall 2021: CSE472 Introduction to Computer Vision, UNIST, Korea Instructor	Aug 2021 - Present
Fall 2021: CSE421 Introduction to Database Systems, UNIST, Korea Instructor	Aug 2021 - Present
Spring 2021: CSE613 3D Shape Analysis in Neuroimaging, UNIST, Korea Instructor	Mar 2021 - June 2021
Fall 2020: CS8395 Open Source Programming for Medical Image Analysis - Surface-based Cortical Shape Analysis, Vanderbilt University, USA Guest Lecturer	Oct 2020
Fall 2019: CS8395 Open Source Programming for Medical Image Analysis - Cortical Surface Reconstruction and Shape Analysis, Vanderbilt University, USA Guest Lecturer	Oct 2019
Fall 2019: CS4262 Foundations of Machine Learning - Machine Learning for Brain Image Analysis, Vanderbilt University, USA Guest Lecturer	Sep 2019
Fall 2017, Fall 2019: SC3250 Scientific Computing - Introduction to Copmuter Vision with Applications, Vanderbilt University, USA Guest Lecturer	Sep 2017, Sep 2019
Summer 2019: Vanderbilt Data Science Institute Summer Research Program (DSI-SRP) , Vanderbilt University, USA Mentor	May 2019 - Jul 2019
Spring 2014: COMP915 Technical Communication in Computer Science, UNC, USA Trainee	Jan 2014 - May 2014
Fall 2010: CS300 Introduction to Algorithms, KAIST, Korea Teaching Assistant	Sep 2010 - Dec 2010
Fall 2009: CS202 Problem Solving, KAIST, Korea Teaching Assistant	Sep 2009 - Dec 2009
Tutoring Program for Freshman: JAVA language, KAIST, Korea Private Tutor	Feb 2008 - Dec 2008

Podium Talks

Hierarchical Spherical Deformation for Shape Correspondence, MICCAI 2018, Granada, Spain	Sep 2018
Sulcal Depth-based Cortical Shape Analysis in Normal Healthy Control and Schizophrenia Groups, SPIE Medical Imaging 2018, Houston, Texas, USA	Feb 2018
Cortical Surface Registration and Shape Analysis, VISE Summer Seminar 2017, Vanderbilt University, Nashville, Tennessee, USA	Jul 2017
Cortical Surface Shape Assessment via Sulcal/Gyral Curve-based Gyrification Index, ISBI 2016, Prague, Czech Republic	Apr 2016
Group-wise Cortical Correspondence via Sulcal Curve-Constrained Entropy Minimization, IPMI 2013, Asilomar, California, USA	Jun 2013

Software

• BISCUIT: Brain Shape Computing Toolbox [Singularity]
• SphericalLabeling: Automatic Labeling of Cortical Surface and Sulci [Singularity]
• CMorph: Shape Analysis Tools for Cortical Morphometry [Docker] [Singularity]

• TRACE: Topological Graph Representation for Automatic Sulcal Curve Extraction
• HSD: Hierarchical Spherical Defomration for Cortical Surface Registration
• LGI: Cortical Shape-Adaptive Local Gyrification Index
• SphericalLabeling: Automatic Labeling of Cortical Surface and Sulci
• SphericalRemesh: Spherical Data Augmentation
• MeshLib: Lightweight General Mesh Library