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Guoqiang Yu

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9 papers

2 author rows

NeurIPS Conference 2023 Conference Paper

NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei Image Segmentation

Wei Zheng
Cheng Peng
Zeyuan Hou
Boyu Lyu
Mengfan Wang
Xuelong Mi
Shuoxuan Qiao
Yinan Wan

3D segmentation of nuclei images is a fundamental task for many biological studies. Despite the rapid advances of large-volume 3D imaging acquisition methods and the emergence of sophisticated algorithms to segment the nuclei in recent years, a benchmark with all cells completely annotated is still missing, making it hard to accurately assess and further improve the performance of the algorithms. The existing nuclei segmentation benchmarks either worked on 2D only or annotated a small number of 3D cells, perhaps due to the high cost of 3D annotation for large-scale data. To fulfill the critical need, we constructed NIS3D, a 3D, high cell density, large-volume, and completely annotated Nuclei Image Segmentation benchmark, assisted by our newly designed semi-automatic annotation software. NIS3D provides more than 22, 000 cells across multiple most-used species in this area. Each cell is labeled by three independent annotators, so we can measure the variability of each annotation. A confidence score is computed for each cell, allowing more nuanced testing and performance comparison. A comprehensive review on the methods of segmenting 3D dense nuclei was conducted. The benchmark was used to evaluate the performance of several selected state-of-the-art segmentation algorithms. The best of current methods is still far away from human-level accuracy, corroborating the necessity of generating such a benchmark. The testing results also demonstrated the strength and weakness of each method and pointed out the directions of further methodological development. The dataset can be downloaded here: https: //github. com/yu-lab-vt/NIS3D.

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NeurIPS Conference 2022 Conference Paper

BILCO: An Efficient Algorithm for Joint Alignment of Time Series

Xuelong Mi
Mengfan Wang
Alex Chen
Jing-Xuan Lim
Yizhi Wang
Misha B Ahrens
Guoqiang Yu

Multiple time series data occur in many real applications and the alignment among them is usually a fundamental step of data analysis. Frequently, these multiple time series are inter-dependent, which provides extra information for the alignment task and this information cannot be well utilized in the conventional pairwise alignment methods. Recently, the joint alignment was modeled as a max-flow problem, in which both the profile similarity between the aligned time series and the distance between adjacent warping functions are jointly optimized. However, despite the new model having elegant mathematical formulation and superior alignment accuracy, the long computation time and large memory usage, due to the use of the existing general-purpose max-flow algorithms, limit significantly its well-deserved wide use. In this report, we present BIdirectional pushing with Linear Component Operations (BILCO), a novel algorithm that solves the joint alignment max-flow problems efficiently and exactly. We develop the strategy of linear component operations that integrates dynamic programming technique and the push-relabel approach. This strategy is motivated by the fact that the joint alignment max-flow problem is a generalization of dynamic time warping (DTW) and numerous individual DTW problems are embedded. Further, a bidirectional-pushing strategy is proposed to introduce prior knowledge and reduce unnecessary computation, by leveraging another fact that good initialization can be easily computed for the joint alignment max-flow problem. We demonstrate the efficiency of BILCO using both synthetic and real experiments. Tested on thousands of datasets under various simulated scenarios and in three distinct application categories, BILCO consistently achieves at least 10 and averagely 20-folds increase in speed, and uses at most 1/8 and averagely 1/10 memory compared with the best existing max-flow method. Our source code can be found at https: //github. com/yu-lab-vt/BILCO.

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ICML Conference 2021 Conference Paper

ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation

Mengfan Wang
Boyu Lyu
Guoqiang Yu

The variance-stabilizing transformation (VST) problem is to transform heteroscedastic data to homoscedastic data so that they are more tractable for subsequent analysis. However, most of the existing approaches focus on finding an analytical solution for a certain parametric distribution, which severely limits the applications, because simple distributions cannot faithfully describe the real data while more complicated distributions cannot be analytically solved. In this paper, we converted the VST problem into a convex optimization problem, which can always be efficiently solved, identified the specific structure of the convex problem, which further improved the efficiency of the proposed algorithm, and showed that any finite discrete distributions and the discretized version of any continuous distributions from real data can be variance-stabilized in an easy and nonparametric way. We demonstrated the new approach on bioimaging data and achieved superior performance compared to peer algorithms in terms of not only the variance homoscedasticity but also the impact on subsequent analysis such as denoising. Source codes are available at https: //github. com/yu-lab-vt/ConvexVST.

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NeurIPS Conference 2019 Conference Paper

muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking

Congchao Wang
Yizhi Wang
Yinxue Wang
Chiung-Ting Wu
Guoqiang Yu

Min-cost flow has been a widely used paradigm for solving data association problems in multi-object tracking (MOT). However, most existing methods of solving min-cost flow problems in MOT are either direct adoption or slight modifications of generic min-cost flow algorithms, yielding sub-optimal computation efficiency and holding the applications back from larger scale of problems. In this paper, by exploiting the special structures and properties of the graphs formulated in MOT problems, we develop an efficient min-cost flow algorithm, namely, minimum-update Successive Shortest Path (muSSP). muSSP is proved to provide exact optimal solution and we demonstrated its efficiency through 40 experiments on five MOT datasets with various object detection results and a number of graph designs. muSSP is always the most efficient in each experiment compared to the three peer solvers, improving the efficiency by 5 to 337 folds relative to the best competing algorithm and averagely 109 to 4089 folds to each of the three peer methods.

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YNIMG Journal 2019 Journal Article

Noninvasive noncontact speckle contrast diffuse correlation tomography of cerebral blood flow in rats

Chong Huang
Siavash Mazdeyasna
Li Chen
Elie G. Abu Jawdeh
Henrietta S. Bada
Kathryn E. Saatman
Lei Chen
Guoqiang Yu

Continuous and longitudinal imaging of cerebral blood flow (CBF) variations provide vital information to investigate pathophysiology and interventions for a variety of neurological and cerebral diseases. An innovative noncontact speckle contrast diffuse correlation tomography (scDCT) system was downscaled and adapted for noninvasive imaging of CBF distributions in rat brain through intact scalp and skull. Algorithms for 2D mapping and 3D image reconstruction of CBF distributions were developed and optimized. The continuous imaging capability of the system was shown by imaging global CBF increases during CO2 inhalations and regional CBF decreases across two hemispheres during sequential unilateral and bilateral common carotid artery ligations. The longitudinal imaging capability was demonstrated by imaging CBF variations over a long recovery period of 14 days after an acute stroke. Compared to the 2D mapping method, the 3D imaging method reduces partial volume effects, but needs more computation time for image reconstruction. Results from this study generally agree with those reported in the literature using similar protocols to induce CBF changes in rats. The scDCT enables a relatively large penetration depth (up to ∼10 mm), which is sufficient for transcranial brain measurements in small animals and human neonates. Ultimately, we expect to provide a noninvasive noncontact cerebral imager for basic neuroscience research in small animal models and clinical applications in human neonates.

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NeurIPS Conference 2016 Conference Paper

Graphical Time Warping for Joint Alignment of Multiple Curves

Yizhi Wang
David Miller
Kira Poskanzer
Yue Wang
Lin Tian
Guoqiang Yu

Dynamic time warping (DTW) is a fundamental technique in time series analysis for comparing one curve to another using a flexible time-warping function. However, it was designed to compare a single pair of curves. In many applications, such as in metabolomics and image series analysis, alignment is simultaneously needed for multiple pairs. Because the underlying warping functions are often related, independent application of DTW to each pair is a sub-optimal solution. Yet, it is largely unknown how to efficiently conduct a joint alignment with all warping functions simultaneously considered, since any given warping function is constrained by the others and dynamic programming cannot be applied. In this paper, we show that the joint alignment problem can be transformed into a network flow problem and thus can be exactly and efficiently solved by the max flow algorithm, with a guarantee of global optimality. We name the proposed approach graphical time warping (GTW), emphasizing the graphical nature of the solution and that the dependency structure of the warping functions can be represented by a graph. Modifications of DTW, such as windowing and weighting, are readily derivable within GTW. We also discuss optimal tuning of parameters and hyperparameters in GTW. We illustrate the power of GTW using both synthetic data and a real case study of an astrocyte calcium movie.

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JBHI Journal 2016 Journal Article

Integrative Analysis of Proteomic, Glycomic, and Metabolomic Data for Biomarker Discovery

Minkun Wang
Guoqiang Yu
Habtom W. Ressom

Studies associating changes in the levels of multiple biomolecules including proteins, glycans, glycoproteins, and metabolites with the onset of cancer have been widely investigated to identify clinically relevant diagnostic biomarkers. Advances in liquid or gas chromatography mass spectrometry (LC-MS, GC-MS) have enabled high-throughput qualitative and quantitative analysis of these biomolecules. While results from separate analyses of different biomolecules have been reported widely, the mutual information obtained by partly or fully combining them has been relatively unexplored. In this study, we investigate integrative analysis of proteins, N-glycans, and metabolites to take advantage of complementary information to improve the ability to distinguish cancer cases from controls. Specifically, support vector machine-recursive feature elimination algorithm is utilized to select a panel of proteins, N-glycans, and metabolites based on LC-MS and GC-MS data previously acquired by the analysis of blood samples from two cohorts in a liver cancer study. Improved performances are observed by integrative analysis compared to separate proteomic, glycomic, and metabolomic studies in distinguishing liver cancer cases from patients with liver cirrhosis.

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YNIMG Journal 2012 Journal Article

Noninvasive optical evaluation of spontaneous low frequency oscillations in cerebral hemodynamics

Ran Cheng
Yu Shang
Don Hayes
Sibu P. Saha
Guoqiang Yu

Spontaneous low frequency oscillations (LFOs) around 0. 1Hz have been observed in mean arterial pressure (MAP) and cerebral blood flow velocity (CBFV). Previous studies have shown that cerebral autoregulation in major arteries can be assessed by quantification of the phase shift between LFOs of MAP and CBFV. However, many cerebral diseases are associated with abnormal microvasculature and tissue dysfunction in brain, and quantification of these abnormalities requires direct measurement of cerebral tissue hemodynamics. This pilot study used a novel hybrid near-infrared diffuse optical instrument to noninvasively and simultaneously detect LFOs of cerebral blood flow (CBF) and cerebral oxygenation (i. e. , oxygenated/deoxygenated/total hemoglobin concentration: [HbO2]/[Hb]/THC) in human prefrontal cortex. Using the hybrid instrument and a finger plethysmograph, the dynamic changes of CBF, [HbO2], [Hb], THC and MAP were concurrently measured in 15 healthy subjects at rest, during 70° head-up-tilting (HUT) and during enforced breathing at 0. 1Hz. The LFOs were extracted from the measured variables using power spectral analysis, and the phase shifts and coherences of LFOs between MAP and each of the measured hemodynamic variables were calculated from the corresponding transfer functions. Levels of coherence (>0. 4) were used to judge the success of LFO measurements. We found that CBF, [HbO2] and THC were reliable hemodynamic parameters in detecting LFOs and HUT was the most robust and stable protocol for quantifying phase shifts of hemodynamic LFOs. Comparing with other relevant studies, similar success rates for detecting cerebral LFOs have been achieved in our study. The phase shifts of LFOs in CBF were also close to those in CBFV reported by other groups, although the results in cerebral oxygenation measurements during enforced breathing varied across studies. Future study will investigate cerebral LFOs in patients with cerebral impairment and evaluate their cerebral autoregulation capabilities and neurocognitive functions via the quantification of LFO phase shifts.

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JMLR Journal 2010 Journal Article

Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases

Guoqiang Yu
Yuanjian Feng
David J. Miller
Jianhua Xuan
Eric P. Hoffman
Robert Clarke
Ben Davidson
Ie-Ming Shih

Microarray gene expressions provide new opportunities for molecular classification of heterogeneous diseases. Although various reported classification schemes show impressive performance, most existing gene selection methods are suboptimal and are not well-matched to the unique characteristics of the multicategory classification problem. Matched design of the gene selection method and a committee classifier is needed for identifying a small set of gene markers that achieve accurate multicategory classification while being both statistically reproducible and biologically plausible. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone (OVE) phenotypic up-regulated genes (PUGs) and matches this gene selection with a one-versus-rest support vector machine (OVRSVM). Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes. Consistent with the OVRSVM structure, we evaluated the fold changes of OVE gene expressions and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed PUG-OVRSVM method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods. The MATLAB toolbox, experiment data and supplement files are available at http://www.cbil.ece.vt.edu/software.htm. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

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