@article {310, title = {Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation}, journal = {Symmetry}, volume = {12}, year = {2020}, pages = {1230}, abstract = {

Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder{\textendash} decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net{\textquoteright}s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34\% of U-Net (7.76 M), 29\% of UNet++ (9.04 M), and 9.1\% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.

}, issn = {2073-8994}, doi = {10.3390/sym12081230}, url = {https://www.mdpi.com/2073-8994/12/8/1230}, author = {Qin, Xiaofei and Wu, Chengzi and Hang Chang and Lu, Hao and Zhang, Xuedian} } @article {301, title = {Machine Learning in Multimodal Medical Imaging.}, journal = {Biomed Res Int}, volume = {2017}, year = {2017}, month = {2017}, pages = {1278329}, keywords = {Animals, Humans, Image Processing, Computer-Assisted, Machine Learning}, issn = {2314-6141}, doi = {10.1155/2017/1278329}, author = {Xia, Yong and Ji, Zexuan and Krylov, Andrey and Hang Chang and Cai, Weidong} } @article {90, title = {Methods for delineating cellular regions and classifying regions of histopathology and microanatomy}, number = {US 14/493,208}, year = {2015}, url = {http://www.google.com/patents/US20150110381}, author = {Parvin, B. and Hang Chang and Zhou, Y.} } @article {194, title = {Mass spectrometry imaging for in situ kinetic histochemistry.}, journal = {Sci Rep}, volume = {3}, year = {2013}, month = {2013}, pages = {1656}, abstract = {

Tissues are composed of diverse cell subpopulations each with distinct metabolic characteristics that influence overall behavior. Unfortunately, traditional histopathology imaging techniques are {\textquoteright}blind{\textquoteright} to the spatially ordered metabolic dynamics within tissue. While mass spectrometry imaging enables spatial mapping of molecular composition, resulting images are only a static snapshot in time of molecules involved in highly dynamic processes; kinetic information of flux through metabolic pathways is lacking. To address this limitation, we developed kinetic mass spectrometry imaging (kMSI), a novel technique integrating soft desorption/ionization mass spectrometry with clinically accepted in vivo metabolic labeling of tissue with deuterium to generate images of kinetic information of biological processes. Applied to a tumor, kMSI revealed heterogeneous spatial distributions of newly synthesized versus pre-existing lipids, with altered lipid synthesis patterns distinguishing region-specific intratumor subpopulations. Images also enabled identification and correlation of metabolic activity of specific lipids found in tumor regions of varying grade.

}, keywords = {Cell Line, Equipment Design, Equipment Failure Analysis, Experimental, Humans, Lipids, Mass Spectrometry, Molecular Imaging, Neoplasms, Tumor}, issn = {2045-2322}, doi = {10.1038/srep01656}, author = {Louie, Katherine B and Bowen, Benjamin P and McAlhany, Stephanie and Huang, Yurong and Price, John C and Jiang-Hua Mao and Hellerstein, Marc and Northen, Trent R} } @inbook {74, title = {Molecular Correlates of Morphometric Subtypes in Glioblastoma Multiforme}, booktitle = {Computational Systems Biology: From Molecular Mechanisms to Disease: Second Edition}, year = {2013}, pages = {423{\textendash}454}, publisher = {Elsevier Inc.}, organization = {Elsevier Inc.}, keywords = {Glioblastoma multiforme, Histology classification, Molecular pathology, Morphometric subtyping, Nuclear segmentation, Tumor heterogeneity, Tumor histopathology}, isbn = {9780124059269}, doi = {10.1016/B978-0-12-405926-9.00018-6}, author = {Hang Chang and Gerald Fontenay and Cemal Cagatay Bilgin and Alexander Borowsky and Paul Spellman and Bahram Parvin} } @conference {34, title = {Molecular bases of morphometric composition in Glioblastoma multiforme}, booktitle = {9th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2012, May 2-5, 2012, Barcelona, Spain, Proceedings}, year = {2012}, doi = {10.1109/ISBI.2012.6235889}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235889}, author = {Ju Han and Hang Chang and Gerald Fontenay and Paul T. Spellman and Alexander Borowsky and Bahram Parvin} } @article {21, title = {Multireference Level Set for the Characterization of Nuclear Morphology in Glioblastoma Multiforme}, journal = {{IEEE} Trans. Biomed. Engineering}, volume = {59}, year = {2012}, pages = {3460{\textendash}3467}, doi = {10.1109/TBME.2012.2218107}, url = {http://dx.doi.org/10.1109/TBME.2012.2218107}, author = {Hang Chang and Ju Han and Paul T. Spellman and Bahram Parvin} } @article {35, title = {Morphometic analysis of {TCGA} glioblastoma multiforme}, journal = {{BMC} Bioinformatics}, volume = {12}, year = {2011}, pages = {484}, doi = {10.1186/1471-2105-12-484}, url = {http://dx.doi.org/10.1186/1471-2105-12-484}, author = {Hang Chang and Gerald Fontenay and Ju Han and Ge Cong and Frederick L. Baehner and Joe W. Gray and Paul T. Spellman and Bahram Parvin} } @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 {87, title = {Molecular predictors of 3D morphogenesis by breast cancer cell lines in 3D culture}, journal = {PLoS Comput Biol}, volume = {6}, year = {2010}, month = {2010 Feb}, pages = {e1000684}, abstract = {

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARgamma has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARgamma has been validated through two supporting biological assays.

}, keywords = {Biological, Biomarkers, Breast Neoplasms, Cell Culture Techniques, Cell Line, Computer-Assisted, ErbB-2, Female, Gene Expression Profiling, Histocytochemistry, Humans, Image Processing, Models, Phenotype, PPAR gamma, Receptor, Reproducibility of Results, Tumor}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000684}, author = {Han, Ju and Hang Chang and Giricz, Orsi and Lee, Genee Y and Baehner, Frederick L and Gray, Joe W and Bissell, Mina J and Kenny, Paraic A and Parvin, Bahram} } @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 {40, title = {Multiphase level set for automated delineation of membrane-bound macromolecules}, booktitle = {Proceedings of the 2010 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands, 14-17 April, 2010}, year = {2010}, doi = {10.1109/ISBI.2010.5490389}, url = {http://dx.doi.org/10.1109/ISBI.2010.5490389}, author = {Hang Chang and Bahram Parvin} } @conference {43, title = {Morphometric Subtyping for a Panel of Breast Cancer Cell Lines}, booktitle = {Proceedings of the 2009 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009}, year = {2009}, doi = {10.1109/ISBI.2009.5193168}, url = {http://dx.doi.org/10.1109/ISBI.2009.5193168}, author = {Ju Han and Hang Chang and Gerald Fontenay and Nicholas J. Wang and Joe W. Gray and Bahram Parvin} } @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} }