Publications by authors named "Yihui Cao"

9 Publications

  • Page 1 of 1

Spinster homolog 2 in cancers, its functions and mechanisms.

Cell Signal 2021 01 2;77:109821. Epub 2020 Nov 2.

School of Medicine, South China University of Technology, Guangzhou, Guandong, 510006, PR China. Electronic address:

Spinster homolog 2 (SPNS2) is a multi-transmembrane transporter, widely located in the cell membrane and organelle membranes. It transports sphingosine-1-phosphate (S1P) into the extracellular space and the circulatory system, thus alters the concentration and the distribution of S1P, sphingosine-1-phosphate receptor (S1PRs) and S1P related enzymes, meaning that it exerts its functions via S1P signaling pathways. Studies also show that ectopic SPNS2 mediates parts of the physiological process of the cells. As of now, SPNS2 has been reported to participate in physiological processes such as angiogenesis, embryonic development, immune response and metabolisms. It is also associated with the transformation from inflammation to cancer as well as the proliferation and metastasis of cancer cells. In this review, we summarize the functions and the mechanisms of SPNS2 in the pathogenesis of cancer to provide new insights for the diagnosis and the treatments of cancer.
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http://dx.doi.org/10.1016/j.cellsig.2020.109821DOI Listing
January 2021

Corner Detection Based Automatic Segmentation of Bioresorbable Vascular Scaffold Struts in IVOCT Images.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:604-607

Bioresorbable Vascular scaffold (BVS) is a promising type of stent in percutaneous coronary intervention. Struts apposition assessment is important to ensure the safety of implanted BVS. Currently, BVS struts apposition analysis in IVOCT images still depends on manual delineation of struts, which is labor intensive and time consuming. Automatic struts segmentation is highly desired to simplify and speed up quantitative analysis. However, it is difficult to segment struts accurately based on the contour, due to the influence of fractures inside strut and blood artifacts around strut. In this paper, a novel framework of automatic struts segmentation based on four corners is introduced, in which priori knowledge is utilized that struts have obvious feature of box-shape. Firstly, a cascaded AdaBoost classifier based on enriched haar-like features is trained to detect struts corners. Then, segmentation result can be obtained based on the four detected corners of each strut. Tested on five pullbacks consisting of 483 images with strut, our novel method achieved an average Dice's coefficient of 0.82 for strut segmentation areas. It concludes that our method can segment struts accurately and robustly. Furthermore, automatic struts malapposition analysis in clinical practice is feasible based on the segmentation results.
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http://dx.doi.org/10.1109/EMBC.2018.8512440DOI Listing
July 2018

Automatic analysis of bioresorbable vascular scaffolds in intravascular optical coherence tomography images.

Biomed Opt Express 2018 Jun 1;9(6):2495-2510. Epub 2018 May 1.

State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, China.

The bioresorbable vascular scaffold (BVS) is a new generation of bioresorbable scaffold (BRS) for the treatment of coronary artery disease. A potential challenge of BVS is malapposition, which may possibly lead to late stent thrombosis. It is therefore important to conduct malapposition analysis right after stenting. Since an intravascular optical coherence tomography (IVOCT) image sequence contains thousands of BVS struts, manual analysis is labor intensive and time consuming. Computer-based automatic analysis is an alternative, but faces some difficulties due to the interference of blood artifacts and the uncertainty of the struts number, position and size. In this paper, we propose a novel framework for a struts malapposition analysis that breaks down the problem into two steps. Firstly, struts are detected by a cascade classifier trained by AdaBoost and a region of interest (ROI) is determined for each strut to completely contain it. Then, strut boundaries are segmented within ROIs through dynamic programming. Based on the segmentation result, malapposition analysis is conducted automatically. Tested on 7 pullbacks labeled by an expert, our method correctly detected 91.5% of 5821 BVS struts with 12.1% false positives. The average segmentation Dice coefficient for correctly detected struts was 0.81. The time consumption for a pullback is 15 on average. We conclude that our method is accurate and efficient for BVS strut detection and segmentation, and enables automatic BVS malapposition analysis in IVOCT images.
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http://dx.doi.org/10.1364/BOE.9.002495DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154186PMC
June 2018

Automatic Side Branch Ostium Detection and Main Vascular Segmentation in Intravascular Optical Coherence Tomography Images.

IEEE J Biomed Health Inform 2018 09 13;22(5):1531-1539. Epub 2017 Nov 13.

Intravascular optical coherence tomography is the state-of-the-art imaging modality in percutaneous coronary intervention planning and evaluation, in which side branch ostium and main vascular measurements play critical roles. However, manual measurement is time consuming and labor intensive. In this paper, we propose a fully automatic method for side branch ostium detection and main vascular segmentation to make up manual deficiency. In our method, side branch ostium points are first detected and subsequently used to divide the lumen contour into side branch and main vascular regions. Based on the division, main vascular contour is then smoothly fitted for segmentation. In side branch ostium detection, our algorithm creatively converts the definition of curvature into the calculation of the signed included angles in global view, and originally applies a differential filter to highlight the feature of side branch ostium points. A total of 4618 images from 22 pullback runs were used to evaluate the performance of the presented method. The validation results of side branch detection were TPR = 82.8 $\%$, TNR = 98.7$\%$ , PPV = 86.8$\%$, NPV = 98.7$\%$. The average ostial distance error (ODE) was 0.22 mm, and the DSC of main vascular segmentation was 0.96. In conclusion, the qualitative and quantitative evaluation indicated that the presented method is effective and accurate.
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http://dx.doi.org/10.1109/JBHI.2017.2771829DOI Listing
September 2018

Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set.

Comput Math Methods Med 2017 7;2017:4710305. Epub 2017 Feb 7.

The State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi 710119, China.

Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was 98.1% ± 1.1%.
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http://dx.doi.org/10.1155/2017/4710305DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320074PMC
April 2017

Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method.

Comput Math Methods Med 2016 22;2016:9514707. Epub 2016 May 22.

Shenzhen Vivolight Medical Device and Technology Co., Ltd., Shenzhen 518000, China.

We propose a dual-threshold method based on a strategic combination of RGB and HSV color space for white blood cell (WBC) segmentation. The proposed method consists of three main parts: preprocessing, threshold segmentation, and postprocessing. In the preprocessing part, we get two images for further processing: one contrast-stretched gray image and one H component image from transformed HSV color space. In the threshold segmentation part, a dual-threshold method is proposed for improving the conventional single-threshold approaches and a golden section search method is used for determining the optimal thresholds. For the postprocessing part, mathematical morphology and median filtering are utilized to denoise and remove incomplete WBCs. The proposed method was tested in segmenting the lymphoblasts on a public Acute Lymphoblastic Leukemia (ALL) image dataset. The results show that the performance of the proposed method is better than single-threshold approach independently performed in RGB and HSV color space and the overall single WBC segmentation accuracy reaches 97.85%, showing a good prospect in subsequent lymphoblast classification and ALL diagnosis.
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http://dx.doi.org/10.1155/2016/9514707DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893444PMC
March 2017

[Analysis of HA and NA Genes of Influenza A H1N1 Virus in Yunnan Province during 2009-2014].

Bing Du Xue Bao 2015 Nov;31(6):674-8

To analyze influenza pathogen spectrum in Yunnan province during 2009-2014 years, and analyze HA and NA genes of influenza A H1N1. Analysis was made on the monitoring date of influenza cases in Yunnan province in recent 6 years, 23 strains of influenza virus of HA and NA gene was sequenced and analyzed by MEGA 5 software to construct phylogenetic tree. 4 times of influenza AH1N1 epidemic peak were monitored from 2009-2014 years in Yunnan Province, as the nucleic acid detection results of influenza A H1N1 accounted for 28.8% of the total. The sequencing result showed that HA and NA gene were divided into 3 groups, one was detected with H275Y mutation strains. Influenza A H1N1 is one of the important subtypes in Yunnan province and their genes have divided into three branches during the period of 2009-2014 years, the vast majority of influenza a H1N1 are still sensitive to neuraminidase inhibitors.
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November 2015

Label image constrained multiatlas selection.

IEEE Trans Cybern 2015 Jun 14;45(6):1158-68. Epub 2014 Nov 14.

Multiatlas based method is commonly used in medical image segmentation. In multiatlas based image segmentation, atlas selection and combination are considered as two key factors affecting the performance. Recently, manifold learning based atlas selection methods have emerged as very promising methods. However, due to the complexity of prostate structures in raw images, it is difficult to get accurate atlas selection results by only measuring the distance between raw images on the manifolds. Although the distance between the regions to be segmented across images can be readily obtained by the label images, it is infeasible to directly compute the distance between the test image (gray) and the label images (binary). This paper tries to address this problem by proposing a label image constrained atlas selection method, which exploits the label images to constrain the manifold projection of raw images. Analyzing the data point distribution of the selected atlases in the manifold subspace, a novel weight computation method for atlas combination is proposed. Compared with other related existing methods, the experimental results on prostate segmentation from T2w MRI showed that the selected atlases are closer to the target structure and more accurate segmentation were obtained by using our proposed method.
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http://dx.doi.org/10.1109/TCYB.2014.2346394DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323590PMC
June 2015

Segmenting images by combining selected atlases on manifold.

Med Image Comput Comput Assist Interv 2011 ;14(Pt 3):272-9

Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, P.R. China.

Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.
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http://dx.doi.org/10.1007/978-3-642-23626-6_34DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370860PMC
November 2011
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