Center for Bioimage Informatics
Center for Bioimage Informatics
McCann M.T.,Carnegie Mellon University |
Ozolek J.A.,University of Pittsburgh |
Ozolek J.A.,Childrens Hospital of Pittsburgh |
Parvin B.,Lawrence Berkeley National Laboratory |
And 2 more authors.
IEEE Signal Processing Magazine | Year: 2015
Histology is the microscopic inspection of plant or animal tissue. It is a critical component in diagnostic medicine and a tool for studying the pathogenesis and biology of processes such as cancer and embryogenesis. Tissue processing for histology has become increasingly automated, drastically increasing the speed at which histology labs can produce tissue slides for viewing. Another trend is the digitization of these slides, allowing them to be viewed on a computer rather than through a microscope. Despite these changes, much of the routine analysis of tissue sections remains a painstaking, manual task that can only be completed by highly trained pathologists at a high cost per hour. There is, therefore, a niche for image analysis methods that can automate some aspects of this analysis. These methods could also automate tasks that are prohibitively time-consuming for humans, e.g., discovering new disease markers from hundreds of whole-slide images (WSIs) or precisely quantifying tissues within a tumor. ©2015IEEE.
Chen S.,Center for Bioimage Informatics |
Lederman G.,Carnegie Mellon University |
Wang Z.,Carnegie Mellon University |
Rizzo P.,University of Pittsburgh |
And 3 more authors.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2014
We propose a novel recovery algorithm for signals with complex, irregular structure that is commonly represented by graphs. Our approach is a generalization of the signal inpainting technique from classical signal processing. We formulate corresponding minimization problems and demonstrate that in many cases they have closed-form solutions. We discuss a relation of the proposed approach to regression, provide an upper bound on the error for our algorithm and compare the proposed technique with other existing algorithms on real-world datasets. © 2014 IEEE.
PubMed | Lincoln Laboratory, Center for Bioimage Informatics, Carnegie Mellon University, Air Force Institute of Technology and University of Pittsburgh
Type: Journal Article | Journal: Journal of pathology informatics | Year: 2014
We propose a methodology for the design of features mimicking the visual cues used by pathologists when identifying tissues in hematoxylin and eosin (H&E)-stained samples.H&E staining is the gold standard in clinical histology; it is cheap and universally used, producing a vast number of histopathological samples. While pathologists accurately and consistently identify tissues and their pathologies, it is a time-consuming and expensive task, establishing the need for automated algorithms for improved throughput and robustness.We use an iterative feedback process to design a histopathology vocabulary (HV), a concise set of features that mimic the visual cues used by pathologists, e.g. cytoplasm color or nucleus density. These features are based in histology and understood by both pathologists and engineers. We compare our HV to several generic texture-feature sets in a pixel-level classification algorithm.Results on delineating and identifying tissues in teratoma tumor samples validate our expert knowledge-based approach.The HV can be an effective tool for identifying and delineating teratoma components from images of H&E-stained tissue samples.
Kolouri S.,Center for Bioimage Informatics |
Basu S.,Center for Bioimage Informatics |
Basu S.,IBM |
Rohde G.K.,Center for Bioimage Informatics |
Rohde G.K.,Carnegie Mellon University
2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 | Year: 2014
Multichannel microscopy has emerged as a technique for imaging multiple targets (molecules, protein distributions, etc.) simultaneously. Discovering the relative changes in these targets (i.e. distribution of different proteins) is fundamental for understanding cell structure and function. We describe a new method for quantifying and visualizing relationships between multiple targets, from a set of segmented multichannel cells. The method is based on combining the canonical correlation analysis technique with a framework for analyzing images based on the concept of optimal mass transportation. We apply the method towards understanding chromatin distribution in cancer nuclei as a function of nuclear envelope shape. We also show that sub cellular distribution of mitochondria can be used to predict the sub cellular localization of actin fibers in yeast cells. Finally, we also describe the application of the method towards understanding relationships between nuclear and cellular shapes in 2D HeLa cells. We believe that the method could serve as a general tool for mining relationships between different sub cellular protein/molecule distributions as well as organelle shapes. © 2014 IEEE.
McCann M.T.,Center for Bioimage Informatics |
Majumdar J.,Center for Bioimage Informatics |
Peng C.,University of Pittsburgh |
Castro C.A.,Carnegie Mellon University |
Kovacevic J.,Center for Bioimage Informatics
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014
In this work, we present a new algorithm and benchmark dataset for stain separation in histology images. Histology is a critical and ubiquitous task in medical practice and research, serving as a gold standard of diagnosis for many diseases. Automating routine histology analysis tasks could reduce health care costs and improve diagnostic accuracy. One challenge in automation is that histology slides vary in their stain intensity and color; we therefore seek a digital method to normalize the appearance of histology images. As histology slides often have multiple stains on them that must be normalized independently, stain separation must occur before normalization. We propose a new digital stain separation method for the universally-used hematoxylin and eosin stain; this method improves on the state-of-the-art by adjusting the contrast of its eosin-only estimate and including a notion of stain interaction. To validate this method, we have collected a new benchmark dataset via chemical destaining containing ground truth images for stain separation, which we release publicly. Our experiments show that our method achieves more accurate stain separation than two comparison methods and that this improvement in separation accuracy leads to improved normalization. © 2014 IEEE.
PubMed | Lane Center for Computational Biology, Center for Bioimage Informatics and Carnegie Mellon University
Type: Journal Article | Journal: Proceedings of the National Academy of Sciences of the United States of America | Year: 2014
Molecular biomarkers are changes measured in biological samples that reflect disease states. Such markers can help clinicians identify types of cancer or stages of progression, and they can guide in tailoring specific therapies. Many efforts to identify biomarkers consider genes that mutate between normal and cancerous tissues or changes in protein or RNA expression levels. Here we define location biomarkers, proteins that undergo changes in subcellular location that are indicative of disease. To discover such biomarkers, we have developed an automated pipeline to compare the subcellular location of proteins between two sets of immunohistochemistry images. We used the pipeline to compare images of healthy and tumor tissue from the Human Protein Atlas, ranking hundreds of proteins in breast, liver, prostate, and bladder based on how much their location was estimated to have changed. The performance of the system was evaluated by determining whether proteins previously known to change location in tumors were ranked highly. We present a number of candidate location biomarkers for each tissue, and identify biochemical pathways that are enriched in proteins that change location. The analysis technology is anticipated to be useful not only for discovering new location biomarkers but also for enabling automated analysis of biomarker distributions as an aid to determining diagnosis.
Wang W.,Center for Bioimage Informatics |
Chen C.,Center for Bioimage Informatics |
Peng T.,Center for Bioimage Informatics |
Slepcev D.,Carnegie Mellon University |
And 2 more authors.
2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings | Year: 2010
We propose a novel method for detecting characteristic informative phenotype patterns from biomedical images. By building a metric space quantifying the difference between images, we learn the distributions of different classes, and then detect the characteristic regions using graph partition. We show that the detected regions are statistically significant. Our approach can also be used for designing differentiating features for specific data set. We apply our method to a digital pathology problem and successfully detect two characteristic phenotypes pertaining to normal liver and hepatoblastoma nuclei. In addition to digital pathology, our method can be applied to other biomedical problems for detecting characteristic phenotypes (e.g. location proteomics, genetic screens, cell mechanics, etc.). ©2010 IEEE.