@article {335, title = {Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning.}, journal = {Neuro Oncol}, year = {2022}, month = {2022 Jun 18}, abstract = {

BACKGROUND: Lower grade gliomas (LGG) are heterogenous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.

METHODS: Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5- year overall survival (OS) of LGG patients. Tumor mutational burden (TMB), and immune cell infiltration between subtypes were analyzed using the Mann-Whitney test. The double-blinded validation for important immunotherapy-related biomarkers were executed using immunohistochemistry (IHC).

RESULTS: We developed a machine learning pipeline to extract CMBs from whole slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multi-center cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by immunohistochemical staining. In addition, the subtypes learned from LGG demonstrates translational impact on glioblastoma (GBM).

CONCLUSIONS: We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune micro-environment, prognosis, and treatment response.

}, issn = {1523-5866}, doi = {10.1093/neuonc/noac154}, author = {Liu, Xiao-Ping and Jin, Xiaoqing and Ahmadian, Saman Seyed and Yang, Xu and Tian, Su-Fang and Cai, Yu-Xiang and Chawla, Kuldeep and Snijders, Antoine M and Xia, Yankai and van Diest, Paul J and Weiss, William A and Mao, Jian-Hua and Li, Zhi-Qiang and Vogel, Hannes and Chang, Hang} } @article {333, title = {Identification of a novel cancer microbiome signature for predicting prognosis of human breast cancer patients.}, journal = {Clin Transl Oncol}, volume = {24}, year = {2022}, month = {2022 Mar}, pages = {597-604}, abstract = {

BACKGROUND: Prognosis of breast cancer (BC) patients differs considerably and identifying reliable prognostic biomarker(s) is imperative. With evidence that the microbiome plays a critical role in the response to cancer therapies, we aimed to identify a cancer microbiome signature for predicting the prognosis of BC patients.

METHODS: The TCGA BC microbiome data (TCGA-BRCA-microbiome) was downloaded from cBioPortal. Univariate and multivariate Cox regression analyses were used to examine association of microbial abundance with overall survival (OS) and to identify a microbial signature for creating a prognostic scoring model. The performance of the scoring model was assessed by the area under the ROC curve (AUC). Nomograms using the microbial signature, clinical factors, and molecular subtypes were established to predict OS and progression-free survival (PFS).

RESULTS: Among 1406 genera, the abundances of 94 genera were significantly associated with BC patient OS in TCGA-BRCA-microbiome dataset. From that set we identified a 15-microbe prognostic signature and developed a 15-microbial abundance prognostic scoring (MAPS) model. Patients in low-risk group significantly prolong OS and PFS as compared to those in high-risk group. The time-dependent ROC curves with MAPS showed good predictive efficacy both in OS and PFS. Moreover, MAPS is an independent prognostic factor for OS and PFS over clinical factors and PAM50-based molecular subtypes and superior to the previously published 12-gene signature. The integration of MAPS into nomograms significantly improved prognosis prediction.

CONCLUSION: MAPS was successfully established to have independent prognostic value, and our study provides a new avenue for developing prognostic biomarkers by microbiome profiling.

}, keywords = {Breast Neoplasms, Female, Humans, Microbiota, Prognosis, Survival Rate}, issn = {1699-3055}, doi = {10.1007/s12094-021-02725-3}, author = {Mao, A W and Barck, H and Young, J and Paley, A and Mao, J -H and Chang, Hang} } @article {332, title = {Prospective Study Reveals Host Microbial Determinants of Clinical Response to Fecal Microbiota Transplant Therapy in Type 2 Diabetes Patients.}, journal = {Front Cell Infect Microbiol}, volume = {12}, year = {2022}, month = {2022}, pages = {820367}, abstract = {

Background: Increasing evidence shows that alterations in gut microbiome (GM) contribute to the development of type 2 diabetes mellitus (T2DM), and fecal microbiota transplantation (FMT) successfully treats various human diseases. However, the benefits of FMT therapy to T2DM patients remain unknown.

Methods: We enrolled 17 patients with T2DM for nonblinded, one-armed intervention trial of FMT. A total of 20 healthy individuals were recruited as the baseline control. HbA1c\% and metabolic parameter change were evaluated in 17 T2DM patients 12 weeks after they received FMT from healthy donors. The GM composition was characterized by 16S rRNA gene amplicon sequencing from fecal samples prior to and 12 weeks after FMT treatment.

Results: We found that the GM of T2DM patients was reconstituted by FMT. We observed a statistically significant decrease in HbA1c\% (from 7.565 {\textpm} 0.148 to 7.190 {\textpm} 0.210, p\<0.01), blood glucose (from 8.483 {\textpm} 0.497 to 7.286 {\textpm} 0.454 mmol/L, p\<0.01), and uric acid (from 309.4 {\textpm} 21.5 to 259.1 {\textpm} 15.8 {\textmu}mol/L, p\<0.01) while a significant increase in postprandial C-peptide (from 4.503 {\textpm} 0.600 to 5.471 {\textpm} 0.728 ng/ml, p\<0.01) at 12 weeks after FMT. Closely evaluating the changes in these assays, we found individual variability in response to FMT treatment. Out of 17 T2DM patients, 11 were found to significantly improve T2DM symptoms. The FMT responders have significantly higher levels of the family and the genus (family ) in their pretreated fecal in comparison to nonresponders, which could predict the clinical response with an area under the curve of 0.83.

Conclusion: Our findings suggest that certain T2DM patients can potentially benefit from FMT, and the pretreated abundance of and in the fecal of patients may serve as potential biomarkers for selecting T2DM patients to receive FMT.

}, keywords = {Diabetes Mellitus, Type 2, Fecal Microbiota Transplantation, Feces, Glycated Hemoglobin A, Humans, Prospective Studies, RNA, Ribosomal, 16S, Treatment Outcome}, issn = {2235-2988}, doi = {10.3389/fcimb.2022.820367}, author = {Ding, Dafa and Yong, Huijuan and You, Na and Lu, Wei and Yang, Xu and Ye, Xiaolong and Wang, Yayun and Cai, Tingting and Zheng, Xiaoling and Chen, Hui and Cui, Bota and Zhang, Faming and Liu, Xingyin and Mao, Jian-Hua and Lu, Yibing and Chang, Hang} } @article {327, title = {Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study}, volume = {129}, year = {2021}, month = {2021/06/01}, pages = {1837 - 1856}, abstract = {

The characterization and classification of white blood cells (WBC) are critical for the diagnosis of anemia, leukemia, and many other hematologic diseases. We developed WBC-Profiler, an unsupervised feature learning system for quantitative analysis of leukocytes. We demonstrate, through independent validation, that WBC-Profiler enables automatic extraction of complex and robust signatures from microscopic images without human-intervention and, thereafter, effective construction of interpretable leukocyte profiles, which decouples large scale complex leukocyte characterization from limitations in both human-based feature engineering/optimization and the end-to-end solutions provided by many modern deep neural networks. Further evaluation in a real-world clinical setting confirms that, compared with 23 clinicians from 8 hospitals (class-average-sensitivity, 0.798; class-average-specificity, 0.963; cell-average-timecost: 3.158\  s), WBC-Profiler performs with significantly improved accuracy and speed (class-average-sensitivity, 0.890; class-average-specificity, 0.980; cell-average-timecost: 0.375\  s). Our findings suggest that WBC-Profiler has the potential clinical implications.

}, isbn = {1573-1405}, url = {https://doi.org/10.1007/s11263-021-01449-9}, author = {Yan, Hong and Mao, Xuanyu and Yang, Xu and Xia, Yongquan and Wang, Chengbin and Wang, Junjun and Xia, Rui and Xu, Xuejing and Wang, Zhiqiang and Li, Zhiyang and Zhao, Xie and Li, Yan and Liu, Guoye and He, Li and Wang, Zhongyu and Wang, Zhiqiong and Li, Zhiqiang and Cai, Weidong and Shen, Han and Chang, Hang} } @article {331, title = {Distinct Clinical Impact and Biological Function of Angiopoietin and Angiopoietin-like Proteins in Human Breast Cancer.}, journal = {Cells}, volume = {10}, year = {2021}, month = {2021 09 29}, abstract = {

Secreted angiopoietin/angiopoietin-like (/) proteins are involved in many biological processes. However, the role of these proteins in human breast cancers (BCs) remains largely unclear. Here, we conducted integrated omics analyses to evaluate the clinical impact of / proteins and to elucidate their biological functions. In BCs, we identified rare mutations in / genes, frequent gains of , , and , and frequent losses of , , and , but observed that , , and were robustly downregulated in multiple datasets. The expression levels of , , and were positively correlated with overall survival (OS), while the expression levels of were negatively correlated with OS. Additionally, the expression levels of and were positively correlated with distant metastasis-free survival (DMFS), while the expression levels of and were negatively correlated with DMFS. The prognostic impacts of / genes depended on the molecular subtypes and on clinical factors. We discovered that various / genes were co-expressed with various genes involved in different pathways. Finally, with the exception of , the remaining genes showed significant correlations with cancer-associated fibroblasts, endothelial cells, and microenvironment score, whereas only was significantly correlated with immune score. Our findings provide strong evidence for the distinct clinical impact and biological function of / proteins, but the question of whether some of them could be potential therapeutic targets still needs further investigation in BCs.

}, keywords = {Angiopoietin-like Proteins, Angiopoietins, Breast Neoplasms, Female, Humans, Prognosis}, issn = {2073-4409}, doi = {10.3390/cells10102590}, author = {Yang, Hui and Zhang, Melody and Mao, Xuan-Yu and Chang, Hang and Perez-Losada, Jesus and Mao, Jian-Hua} } @article {323, title = {From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer.}, journal = {Front Oncol}, volume = {11}, year = {2021}, month = {2021}, pages = {819565}, abstract = {

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H\&E) micrographs of orthotopic -null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan-Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H\&E preparations can provide unbiased biological stratification to inform patient care.

}, issn = {2234-943X}, doi = {10.3389/fonc.2021.819565}, author = {Chang, Hang and Yang, Xu and Moore, Jade and Liu, Xiao-Ping and Jen, Kuang-Yu and Snijders, Antoine M and Ma, Lin and Chou, William and Corchado-Cobos, Roberto and Garc{\'\i}a-Sancha, Natalia and Mendiburu-Eli{\c c}abe, Marina and Perez-Losada, Jesus and Barcellos-Hoff, Mary Helen and Mao, Jian-Hua} } @article {329, title = {Genetic background influences the effect of thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice.}, journal = {Sci Rep}, volume = {11}, year = {2021}, month = {2021 06 24}, pages = {13285}, abstract = {

Growing evidence indicates that thirdhand smoke (THS) exposure induces many adverse health effects. However, it is unclear how THS exposure affects behavior and how host genetic background modulates phenotypic changes. Here we used the Collaborative Cross (CC) mouse population-based model to assess behavioral alterations immediately after THS exposure from 4 to 9\ weeks of age. We first measured anxiety-like behavior in six strains using light/dark box combined with a custom multivariate mouse tracking system. We developed an anxiety risk scoring system based on anxiety-related traits and then evaluated the THS impact on them. THS exposure significantly decreased anxiety risk in CC019 (P = 0.002) and CC051 (P = 0.009), but increased anxiety risk in CC036 (P \< 0.001), while the other three strains did not show significant changes in anxiety-related traits. Such differences were driven by female mice for the six measures of anxiety-like behavior. Memory potential was measured in the same cohort of mice using the passive avoidance assay. Both THS-exposed male and female CC019 mice displayed significant memory loss compared to controls while no significant changes were found in the other five strains. This study provides strong evidence that THS exposure leads to strain-dependent changes in anxiety-like behavior and memory, suggesting that host genetic variations play a critical role in individual susceptibility to THS-induced effects.

}, keywords = {Animals, Anxiety, Collaborative Cross Mice, Female, Humans, Male, Memory, Risk Factors, Tobacco Smoke Pollution}, issn = {2045-2322}, doi = {10.1038/s41598-021-92702-1}, author = {He, Li and Wang, Pin and Schick, Suzyann F and Huang, Abel and Jacob, Peyton and Yang, Xu and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua and Chang, Hang and Hang, Bo} } @article {325, title = {Host genetics and gut microbiota cooperatively contribute to azoxymethane-induced acute toxicity in Collaborative Cross mice.}, journal = {Arch Toxicol}, volume = {95}, year = {2021}, month = {2021 03}, pages = {949-958}, abstract = {

Azoxymethane (AOM) is a widely used carcinogen to study chemical-induced colorectal carcinogenesis and is an agent for studying fulminant hepatic failure. The inter-strain susceptibility to acute toxicity by AOM has been reported, but its association with host genetics or gut microbiota remains largely unexplored. Here a cohort of genetically diverse Collaborative Cross (CC) mice was used to assess the contribution of host genetics and the gut microbiome to AOM-induced acute toxicity. We observed variation in AOM-induced acute liver failure across CC strains. Quantitative trait loci (QTL) analysis revealed three chromosome regions significantly associated with AOM toxicity. Genes located within these QTL, including peroxisome proliferator-activated receptor alpha (Ppara), were enriched for enzyme activator and nucleoside-triphosphatase regulator activity. We further demonstrated that the protein level of PPARα in liver tissues from sensitive strains was remarkably lower compared to levels in resistant strains, consistent with protective role of PPAR family in liver injury. We discovered that the abundance levels of gut microbial families Anaeroplasmataceae, Ruminococcaceae, Lactobacillaceae, Akkermansiaceae and Clostridiaceae were significantly higher in the sensitive strains\ compared to the resistant strains. Using a random forest classifier method, we determined that the relative abundance levels of these microbial families predicted AOM toxicity with the area under the receiver-operating curve (AUC) of 0.75. Combining the three genetic loci and five microbial families increased the predictive accuracy of AOM toxicity (AUC of 0.99). Moreover, we found that Ruminococcaceae and Lactobacillaceae acted as mediators between host genetics and AOM toxicity. In conclusion, this study shows that host genetics and specific microbiome members play a critical role in AOM-induced acute toxicity, which provides a framework for analysis of the health effects from environmental toxicants.

}, keywords = {Animals, Azoxymethane, Carcinogens, Chemical and Drug Induced Liver Injury, Collaborative Cross Mice, Gastrointestinal Microbiome, Liver Failure, Acute, Male, Mice, Quantitative Trait Loci, Species Specificity}, issn = {1432-0738}, doi = {10.1007/s00204-021-02972-x}, author = {Zhong, Chenhan and He, Li and Lee, Sun-Young and Chang, Hang and Zhang, Yuqing and Threadgill, David W and Yuan, Ying and Zhou, Fuling and Celniker, Susan E and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua} } @article {322, title = {Identification of a novel 15-gene expression signature predicting overall survival of human colorectal cancer.}, journal = {Clin Transl Med}, volume = {10}, year = {2020}, month = {2020 Dec}, pages = {e258}, issn = {2001-1326}, doi = {10.1002/ctm2.258}, author = {Jiang, Chengfei and Zhao, Yue and Yuan, Binbin and Chang, Hang and Hang, Bo and Snijders, Antoine M and Mao, Jian-Hua and Zou, Xiaoping and Wang, Pin} } @article {204, title = {Hematein, a casein kinase II inhibitor, inhibits lung cancer tumor growth in a murine xenograft model.}, journal = {Int J Oncol}, volume = {43}, year = {2013}, month = {2013 Nov}, pages = {1517-22}, abstract = {

Casein kinase II (CK2) inhibitors suppress cancer cell growth. In this study, we examined the inhibitory effects of a novel CK2 inhibitor, hematein, on tumor growth in a murine xenograft model. We found that in lung cancer cells, hematein inhibited cancer cell growth, Akt/PKB Ser129 phosphorylation, the Wnt/TCF pathway and increased apoptosis. In a murine xenograft model of lung cancer, hematein inhibited tumor growth without significant toxicity to the mice tested. Molecular docking showed that hematein binds to CK2α in durable binding sites. Collectively, our results suggest that hematein is an allosteric inhibitor of protein kinase CK2 and has antitumor activity to lung cancer.

}, keywords = {Animals, Apoptosis, Blotting, Casein Kinase II, Cultured, Enzyme Inhibitors, Female, Hematoxylin, Humans, Immunoenzyme Techniques, Inbred BALB C, Lung Neoplasms, Mice, Phosphorylation, Proto-Oncogene Proteins c-akt, T Cell Transcription Factor 1, Tumor Cells, Western, Wnt Proteins, Xenograft Model Antitumor Assays}, issn = {1791-2423}, doi = {10.3892/ijo.2013.2087}, author = {Hung, Ming-Szu and Xu, Zhidong and Chen, Yu and Smith, Emmanuel and Jiang-Hua Mao and Hsieh, David and Lin, Yu-Ching and Yang, Cheng-Ta and Jablons, David M and You, Liang} } @article {85, title = {Multiscale iterative voting for differential analysis of stress response for 2D and 3D cell culture models}, journal = {J Microsc}, volume = {241}, year = {2011}, month = {2011 Mar}, pages = {315-26}, abstract = {

Three-dimensional (2D) cell culture models have emerged as the basis for improved cell systems biology. However, there is a gap in robust computational techniques for segmentation of these model systems that are imaged through confocal or deconvolution microscopy. The main issues are the volume of data, overlapping subcellular compartments and variation in scale or size of subcompartments of interest, which lead to ambiguities for quantitative analysis on a cell-by-cell basis. We address these ambiguities through a series of geometric operations that constrain the problem through iterative voting and decomposition strategies. The main contributions of this paper are to (i) extend the previously developed 2D radial voting to an efficient 3D implementation, (ii) demonstrate application of iterative radial voting at multiple subcellular and molecular scales, and (iii) investigate application of the proposed technology to two endpoints between 2D and 3D cell culture models. These endpoints correspond to kinetics of DNA damage repair as measured by phosphorylation of γH2AX, and the loss of the membrane-bound E-cadherin protein as a result of ionizing radiation. Preliminary results indicate little difference in the kinetics of the DNA damage protein between 2D and 3D cell culture models; however, differences between membrane-bound E-cadherin are more pronounced.

}, keywords = {Cell Culture Techniques, Cell Line, Computer-Assisted, Epithelial Cells, Humans, Image Processing, Microscopy, Organ Culture Techniques, Physiological, Stress}, issn = {1365-2818}, doi = {10.1111/j.1365-2818.2010.03442.x}, author = {Ju Han and Hang Chang and Yang, Q and Fontenay, G and Groesser, Torsten and Barcellos-Hoff, M Helen and Parvin, B} } @article {88, title = {Multidimensional profiling of cell surface proteins and nuclear markers}, journal = {IEEE/ACM Trans Comput Biol Bioinform}, volume = {7}, year = {2010}, month = {2010 Jan-Mar}, pages = {80-90}, abstract = {

Cell membrane proteins play an important role in tissue architecture and cell-cell communication. We hypothesize that segmentation and multidimensional characterization of the distribution of cell membrane proteins, on a cell-by-cell basis, enable improved classification of treatment groups and identify important characteristics that can otherwise be hidden. We have developed a series of computational steps to 1) delineate cell membrane protein signals and associate them with a specific nucleus; 2) compute a coupled representation of the multiplexed DNA content with membrane proteins; 3) rank computed features associated with such a multidimensional representation; 4) visualize selected features for comparative evaluation through heatmaps; and 5) discriminate between treatment groups in an optimal fashion. The novelty of our method is in the segmentation of the membrane signal and the multidimensional representation of phenotypic signature on a cell-by-cell basis. To test the utility of this method, the proposed computational steps were applied to images of cells that have been irradiated with different radiation qualities in the presence and absence of other small molecules. These samples are labeled for their DNA content and E-cadherin membrane proteins. We demonstrate that multidimensional representations of cell-by-cell phenotypes improve predictive and visualization capabilities among different treatment groups, and identify hidden variables.

}, keywords = {Biological, Cadherins, Cell Membrane, Cell Nucleus, Computer Simulation, DNA, Gene Expression Profiling, Membrane Proteins, Models}, issn = {1557-9964}, doi = {10.1109/TCBB.2008.134}, author = {Ju Han and Hang Chang and Andarawewa, Kumari and Yaswen, Paul and Barcellos-Hoff, Mary Helen and Parvin, Bahram} } @conference {45, title = {A Bayesian approach for image segmentation with shape priors}, booktitle = {2008 {IEEE} Computer Society Conference on Computer Vision and Pattern Recognition {(CVPR} 2008), 24-26 June 2008, Anchorage, Alaska, {USA}}, year = {2008}, doi = {10.1109/CVPR.2008.4587430}, url = {http://dx.doi.org/10.1109/CVPR.2008.4587430}, author = {Hang Chang and Qing Yang and Bahram Parvin} } @conference {47, title = {Integrated profiling of cell surface protein and nuclear marker for discriminant analysis}, booktitle = {Proceedings of the 2008 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14-17, 2008}, year = {2008}, doi = {10.1109/ISBI.2008.4541253}, url = {http://dx.doi.org/10.1109/ISBI.2008.4541253}, author = {Ju Han and Hang Chang and Kumari L. Andarawewa and Paul Yaswen and Mary Helen Barcellos-Hoff and Bahram Parvin} } @article {49, title = {Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events}, journal = {{IEEE} Trans. Image Processing}, volume = {16}, year = {2007}, pages = {615{\textendash}623}, doi = {10.1109/TIP.2007.891154}, url = {http://dx.doi.org/10.1109/TIP.2007.891154}, author = {Bahram Parvin and Qing Yang and Ju Han and Hang Chang and Bjorn Rydberg and Mary Helen Barcellos-Hoff} } @conference {50, title = {Modeling of Front Evolution with Graph Cut Optimization}, booktitle = {Proceedings of the International Conference on Image Processing, {ICIP} 2007, September 16-19, 2007, San Antonio, Texas, {USA}}, year = {2007}, doi = {10.1109/ICIP.2007.4378936}, url = {http://dx.doi.org/10.1109/ICIP.2007.4378936}, author = {Hang Chang and Qing Yang and Manfred Auer and Bahram Parvin} } @article {48, title = {Segmentation of heterogeneous blob objects through voting and level set formulation}, journal = {Pattern Recognition Letters}, volume = {28}, year = {2007}, pages = {1781{\textendash}1787}, doi = {10.1016/j.patrec.2007.05.008}, url = {http://dx.doi.org/10.1016/j.patrec.2007.05.008}, author = {Hang Chang and Qing Yang and Bahram Parvin} } @conference {52, title = {Segmentation of Mammosphere Structures from Volumetric Data}, booktitle = {Proceedings of the 2007 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Washington, DC, USA, April 12-16, 2007}, year = {2007}, doi = {10.1109/ISBI.2007.356904}, url = {http://dx.doi.org/10.1109/ISBI.2007.356904}, author = {Ju Han and Hang Chang and Qing Yang and Mary Helen Barcellos-Hoff and Bahram Parvin} } @conference {55, title = {3D Segmentation of Mammospheres for Localization Studies}, booktitle = {Advances in Visual Computing, Second International Symposium, {ISVC} 2006, Lake Tahoe, NV, USA, November 6-8, 2006 Proceedings, Part {I}}, year = {2006}, doi = {10.1007/11919476_52}, url = {http://dx.doi.org/10.1007/11919476_52}, author = {Ju Han and Hang Chang and Qing Yang and Mary Helen Barcellos-Hoff and Bahram Parvin} } @conference {54, title = {An Iterative Bayesian Approach for Digital Matting}, booktitle = {18th International Conference on Pattern Recognition {(ICPR} 2006), 20-24 August 2006, Hong Kong, China}, year = {2006}, doi = {10.1109/ICPR.2006.259}, url = {http://dx.doi.org/10.1109/ICPR.2006.259}, author = {Hang Chang and Qing Yang and Chunhong Pan} }