High-performance Visual Computing Lab (HVCL) at Korea University focuses on developing novel visual computing algorithms and visualization systems for scientific discoveries. Specifically, our research interests lie across diverse research fields such as Image Processing, Visualization, Machine Learning, and Computer Graphics.
We are looking for self-motivated undergrad/graduate students and postdoc researchers who are interested in visual computing. If you want to join our lab, please contact Prof. Won-Ki Jeong (wkjeong@korea.ac.kr) !

Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model

PatHME: Hierarchical Multi-Expert Knowledge Distillation for Whole Slide Image Analysis

HID-CON: weakly supervised intrahepatic cholangiocarcinoma subtype classification of whole slide images using contrastive hidden class detection

MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations

XROps: A Visual Workflow Management System for Dynamic Immersive Analytics

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification

Optimal Dimensionality Selection Using Hull Heatmaps for Single-Cell Analysis

S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning

Intuitive Design of Deep Learning Models through Visual Feedback

Co-synthesis of Histopathology Nuclei Image-Label Pairs using a Context-Conditioned Joint Diffusion Model

Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models

Reference-free Axial Super-resolution of 3D Microscopy Images using Implicit Neural Representation with a 2D Diffusion Prior

Clinical-grade Multi-Organ Pathology Report Generation for Multi-scale Whole Slide Images via a Semantically Guided Medical Text Foundation Model

Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity

Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models

Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation

Breaking Down the Technical Barrier: Visual Programming Driven Dynamic Immersive Analytics using XROps

ZoomISEG: Interactive Multi-Scale Fusion for Histopathology Whole Slide Image Segmentation

PAIP 2020: Microsatellite instability prediction in colorectal cancer

Histopathology Image Classification using Deep Manifold Contrastive Learning

DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets

Dimensionality Explorer for Single-Cell Analysis

Multi-Scale Contrastive Learning with Attention for Histopathology Image Classification

CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement

Undersampled MRI Reconstruction using Switchable Interdependent Self-Cooperative Learning without Paired Training Data

MitoVis: A Unified Visual Analytics System for End-to-End Neuronal Mitochondria Analysis

Biomolecular condensate assembly of nArgBP2 tunes its functionality to manifest the structural plasticity of dendritic spines

RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction

Virtual Air Conditioner’s Airflow Simulation and Visualization in AR

DXplorer: A Unified Visualization Framework for Interactive Dendritic Spine Analysis using 3D Morphological Features

Noise2Kernel: Adaptive Self-Supervised Blind Denoising Using a Dilated Convolutional Kernel Architecture

HoloInset: 3D Biomedical Image Data Exploration through Augmented Hologram Insets

MG-FIM: A Multi-GPU FIM using Adaptive Domain Decomposition

Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization

Neuron Tracing-and Deep Learning-guided Interactive Proofreading for Neuron Structure Segmentation

ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising

Development and operation of a digital platform for sharing pathology image data

ZeVis: A Visual Analytics System for Exploration of a Larval Zebrafish Brain in Serial-Section Electron Microscopy Images

Quantitative three-dimensional image analysis of the superior canal after surgical plugging to treat superior semicircular canal dehiscence

ColorRL: Reinforced Coloring for End-to-End Instance Segmentation

Improving the Accuracy of Intrahepatic Cholangiocarcinoma Subtype Classification by Hidden Class Detection via Label Smoothing

DeepScribble: Interactive Pathology Image Segmentation Using Deep Neural Networks with Scribbles

Neuron Segmentation using Incomplete and Noisy Labels via Adaptive Learning with Structure Priors

DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

Assessment of the Impact of Sand Mining on Bottom Morphology in the Mekong River in An Giang Province, Vietnam, Using a Hydro-Morphological Model with GPU Computing

PAIP 2019: Liver Cancer Segmentation Challenge

Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency

Distributed Interactive Visualization using GPU-Optimized Spark

Removing Imaging Artifacts in Electron Microscopy using an Asymmetrically Cyclic Adversarial Network without Paired Training Data

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network

Interactive Dendritic Spine Analysis Based on 3D Morphological Features

Gadget Arms: Interactive Data Visualization using Hand Gesture in Extended Reality

Frequency-splitting Dynamic MRI Reconstruction using Multi-scale 3D Convolutional Sparse Coding and Automatic Parameter Selection.

Weakly Supervised Learning in Deformable EM Image Registration using Slice Interpolation

DXR: A Toolkit for Building Immersive Data Visualizations

Compressed Sensing MRI Reconstruction using Generative Adversarial Networks with Cyclic Loss

Improvement Depth Perception of Volume Rendering using Virtual Reality

DeepHCS: Bright-Field to Fluorescence Microscopy Image Conversion Using Deep Learning for Label-Free High-Content Screening

Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss

Compressed sensing dynamic MRI reconstruction using multi-scale 3D convolutional sparse coding with elastic net regularization

Probabilistic Volume Rendering using Hierarchical 3D Convolutional Sparse Coding

ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features

t-SNE based Clustering Analysis for Drug Response Measurement of Glioblastoma

An Intelligent System Approach for Robust Volume Rendering using Hierarchical 3D Convolutional Sparse Coding

GPU in-memory processing using Spark for iterative computation

Whole-brain serial-section electron microscopy in larval zebrafish

A Group-Ordered Fast Iterative Method for Eikonal Equations

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

명시야 현미경 영상에서의 세포 분할을위한 이중 사전 학습 기법

Compressed Sensing Dynamic MRI Reconstruction Using GPU-accelerated 3D Convolutional Sparse Coding

A Multi-GPU Fast Iterative Method for Eikonal Equations Using On-the-fly Adaptive Domain Decomposition.

A Fast Discrete Wavelet Transform Using Hybrid Parallelism on GPUs

Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding

Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss

Fusionnet: A deep fully residual convolutional neural network for image segmentation in connectomics

DXR: A toolkit for building immersive data visualizations

A fast iterative method for eikonal equations

Scribble2label: Scribble-supervised cell segmentation via self-generating pseudo-labels with consistency

PAIP 2019: Liver cancer segmentation challenge

Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach

Using growing cell structures for surface reconstruction

Scalable and interactive segmentation and visualization of neural processes in EM datasets

Ssecrett and neurotrace: interactive visualization and analysis tools for large-scale neuroscience data sets

Direct reconstruction of a displaced subdivision surface from unorganized points

ssEMnet: Serial-section electron microscopy image registration using a spatial transformer network with learned features

Interactive visualization of volumetric white matter connectivity in DT-MRI using a parallel-hardware Hamilton-Jacobi solver

A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI

A fast iterative method for solving the eikonal equation on triangulated surfaces

Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets

Interactive 3D seismic fault detection on the graphics hardware

Exploring the connectome: Petascale volume visualization of microscopy data streams

ISCL: Interdependent self-cooperative learning for unpaired image denoising

Hanspeter Pfister

Ross Whitaker

David Grant Colburn Hildebrand

Markus Hadwiger

Johanna Beyer

Hans-Peter Seidel

Ioannis Ivrissimtzis

Tom Fletcher

Seungyong Lee

Jens Schneider

Robert M. Kirby OR R.M. Kirby

Zhisong Fu

Horst Bischof

Westermann Rüdiger

Man Hee Lee

Beomseok Nam

Jinwoong Kim

Sylvain Paris

Jan Kautz

Tolga Tasdizen

Won-Ki Jeong

Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fullyresidual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given …

High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish ( Danio rerio ) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging …

Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Deep learning has provided exceptional performance in image classification tasks in computer vision, leading to a recent explosion in popularity. Similarly, its application to connectomic analyses holds great promise. Here, we introduce a deep neural network architecture, FusionNet, with a focus on its application to accomplish automatic segmentation of neuronal structures in connectomics data. FusionNet combines recent advances in machine learning, such as semantic segmentation and residual neural networks, with summation-based skip connections. This results in a much deeper network architecture and improves segmentation accuracy. We demonstrate the performance of the proposed method by comparing it with several other popular electron microscopy segmentation methods. We further illustrate its flexibility through segmentation results for two different tasks: cell membrane segmentation and cell nucleus segmentation.

This paper presents DXR, a toolkit for building immersive data visualizations based on the Unity development platform. Over the past years, immersive data visualizations in augmented and virtual reality (AR, VR) have been emerging as a promising medium for data sense-making beyond the desktop. However, creating immersive visualizations remains challenging, and often require complex low-level programming and tedious manual encoding of data attributes to geometric and visual properties. These can hinder the iterative idea-to-prototype process, especially for developers without experience in 3D graphics, AR, and VR programming. With DXR, developers can efficiently specify visualization designs using a concise declarative visualization grammar inspired by Vega-Lite. DXR further provides a GUI for easy and quick edits and previews of visualization designs in-situ, i.e., while immersed in the virtual world …

In this paper we propose a novel computational technique to solve the Eikonal equation efficiently on parallel architectures. The proposed method manages the list of active nodes and iteratively updates the solutions on those nodes until they converge. Nodes are added to or removed from the list based on a convergence measure, but the management of this list does not entail an extra burden of expensive ordered data structures or special updating sequences. The proposed method has suboptimal worst-case performance but, in practice, on real and synthetic datasets, runs faster than guaranteed-optimal alternatives. Furthermore, the proposed method uses only local, synchronous updates and therefore has better cache coherency, is simple to implement, and scales efficiently on parallel architectures. This paper describes the method, proves its consistency, gives a performance analysis that compares the …

Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-intensive to generate. In this paper, we introduce Scribble2Label , a novel weakly-supervised cell segmentation framework that exploits only a handful of scribble annotations without full segmentation labels. The core idea is to combine pseudo-labeling and label filtering to generate reliable labels from weak supervision. For this, we leverage the consistency of predictions by iteratively averaging the predictions to improve pseudo labels. We demonstrate the performance of Scribble2Label by comparing it to several state-of-the-art cell segmentation …

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were …

This paper presents the first volume visualization system that scales to petascale volumes imaged as a continuous stream of high-resolution electron microscopy images. Our architecture scales to dense, anisotropic petascale volumes because it: (1) decouples construction of the 3D multi-resolution representation required for visualization from data acquisition, and (2) decouples sample access time during ray-casting from the size of the multi-resolution hierarchy. Our system is designed around a scalable multi-resolution virtual memory architecture that handles missing data naturally, does not pre-compute any 3D multi-resolution representation such as an octree, and can accept a constant stream of 2D image tiles from the microscopes. A novelty of our system design is that it is visualization-driven: we restrict most computations to the visible volume data. Leveraging the virtual memory architecture, missing data are …

We study the use of neural network algorithms in surface reconstruction from an unorganized point cloud, and meshing of an implicit surface. We found that for such applications, the most suitable type of neural networks is a modified version of the growing cell structure we propose here. The algorithm works by sampling randomly a target space, usually a point cloud or an implicit surface, and adjusting accordingly the neural network. The adjustment includes the connectivity of the network. Doing several experiments we found that the algorithm gives satisfactory results in some challenging situations involving sharp features and concavities. Another attractive feature of the algorithm is that its speed is virtually independent of the size of the input data, making it particularly suitable for the reconstruction of a surface from a very large point set.

Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuro-scientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on …

Recent advances in optical and electron microscopy let scientists acquire extremely high-resolution images for neuroscience research. Data sets imaged with modern electron microscopes can range between tens of terabytes to about one petabyte. These large data sizes and the high complexity of the underlying neural structures make it very challenging to handle the data at reasonably interactive rates. To provide neuroscientists flexible, interactive tools, the authors introduce Ssecrett and NeuroTrace, two tools they designed for interactive exploration and analysis of large-scale optical- and electron-microscopy images to reconstruct complex neural circuits of the mammalian nervous system.

In this paper we describe the generation of a displaced subdivision surface directly from a set of unorganized points. The displaced subdivision surface is an efficient mesh representation that defines a detailed mesh with a displacement map over a smooth domain surface and has many benefits including compression, rendering, and animation, which overcome limitations of an irregular mesh produced by an ordinary mesh reconstruction scheme. Unlike previous displaced subdivision surface reconstruction methods, our method does not rely on a highly detailed reconstructed mesh. Instead, we efficiently create a coarse base mesh, which is used to sample displacements directly from unorganized points, and this results in a simple process and fast calculation. We suggest a shrink-wrapping-like shape approximation and a point-based mesh simplification method that uses the distance between a set of points and a …

The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits. However, each ssEM plane contains densely packed structures that vary from one section to the next, which makes matching features across images a challenge. Advances in deep learning has resulted in unprecedented performance in similar computer vision problems, but to our knowledge, they have not been successfully applied to ssEM image co-registration. In this paper, we introduce a novel deep network model that combines a spatial transformer for image deformation and a convolutional autoencoder for unsupervised feature learning for robust ssEM image alignment. This results in improved accuracy and robustness while requiring substantially less user intervention than conventional methods. We evaluate our method by comparing registration quality across …

In this paper we present a method to compute and visualize volumetric white matter connectivity in diffusion tensor magnetic resonance imaging (DT-MRI) using a Hamilton-Jacobi (H-J) solver on the GPU (Graphics Processing Unit). Paths through the volume are assigned costs that are lower if they are consistent with the preferred diffusion directions. The proposed method finds a set of voxels in the DTI volume that contain paths between two regions whose costs are within a threshold of the optimal path. The result is a volumetric optimal path analysis, which is driven by clinical and scientific questions relating to the connectivity between various known anatomical regions of the brain. To solve the minimal path problem quickly, we introduce a novel numerical algorithm for solving H-J equations, which we call the Fast Iterative Method (FIM). This algorithm is well-adapted to parallel architectures, and we present a …

In this paper we present a volumetric approach for quantitatively studying white matter connectivity from diffusion tensor magnetic resonance imaging (DT-MRI). The proposed method is based on a minimization of path cost between two regions, defined as the integral of local costs that are derived from the full tensor data along the path. We solve the minimal path problem using a Hamilton-Jacobi formulation of the problem and a new, fast iterative method that computes updates on the propagating front of the cost function at every point. The solutions for the fronts emanating from the two initial regions are combined, giving a voxel-wise connectivity measurement of the optimal paths between the regions that pass through those voxels. The resulting high-connectivity voxels provide a volumetric representation of the white matter pathway between the terminal regions. We quantify the tensor data along these pathways …

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton–Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal , but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput. , 30 (2008), pp. 2512–2534], the authors proposed the fast iterative method (FIM) to efficiently solve the …

Nuclei segmentation and classification is an important process in pathological image analysis. Deep learning-based approaches contribute significantly to the enhanced accuracy of this task. However, these approaches suffer from an imbalanced nuclei data composition, which results in lower classification performance for rare nuclei class. In this study, we proposed a realistic data synthesis method using a diffusion model. We generated two types of virtual patches to enlarge the training data distribution, which balanced the nuclei class variance and increased the chance of investigating various nuclei. Subsequently, we used a semantic label-conditioned diffusion model to generate realistic and high-quality image samples. We demonstrated the efficacy of our method based on experimental results on two imbalanced nuclei datasets, improving the state-of-the-art networks. The experimental results suggest that the …

This paper presents a 3D, volumetric, seismic fault detection system that relies on a novel set of nonlinear filters combined with a GPU (Graphics Processing Unit) implementation, which makes interactive nonlinear, volumetric processing feasible. The method uses a 3D structure tensor to robustly measure seismic orientations. These tensors guide an anisotropic diffusion, which reduces noise in the data while enhancing the fault discontinuity and coherency along seismic strata. A fault-likelihood volume is computed using a directional variance measure, and 3D fault voxels are then extracted through a non-maximal-suppression method. We also show how the proposed algorithms are efficiently implemented with a GPU programming model, and compare this to a CPU implementation to show the benefits of using the GPU for this computationally demanding problem.

Recent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams. The system can concurrently handle multiple volumes and can support the simultaneous visualization of high-resolution voxel segmentation data. Its visualization-driven design restricts most computations to a small subset of the data. It employs a multiresolution virtual-memory architecture for better scalability than previous approaches and for handling incomplete data. Researchers have employed it for a 1-teravoxel mouse cortex volume, of which several hundred axons and …

With the advent of advances in self-supervised learning, paired clean-noisy data are no longer required in deep learning-based image denoising. However, existing blind denoising methods still require the assumption with regard to noise characteristics, such as zero-mean noise distribution and pixel-wise noise-signal independence; this hinders wide adaptation of the method in the medical domain. On the other hand, unpaired learning can overcome limitations related to the assumption on noise characteristics, which makes it more feasible for collecting the training data in real-world scenarios. In this paper, we propose a novel image denoising scheme, Interdependent Self-Cooperative Learning (ISCL), that leverages unpaired learning by combining cyclic adversarial learning with self-supervised residual learning. Unlike the existing unpaired image denoising methods relying on matching data distributions in …

High-performance Visual Computing Lab (HVCL)

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Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model

Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model

PatHME: Hierarchical Multi-Expert Knowledge Distillation for Whole Slide Image Analysis

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저널 & 학회 논문 출판 수

연구실 논문 목록

Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model

Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model

PatHME: Hierarchical Multi-Expert Knowledge Distillation for Whole Slide Image Analysis

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