Research in Microscopic Imaging
Table of Content
Abstract
Contact Person and Group Coordination
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Tingying Peng |
Nassir Navab |
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Research Projects in Microscopic Imaging
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Knowledge Propagation Models for Image RegistrationTo register modalities with complex intensity relationships, we leverage machine learning algorithm to cast it into a mono modal registration problem. This is done by extracting tissue specific features for propagating anatomical/structural knowledge from one modalitiy to an other through an online learnt propagation model. The registration and propagation steps are iteratively performed and refined. For proof-of-concept, we employ it for registering (1) Immunofluorescence to Histology images and (2) Intravascular Ultrasound to Histology Images. |
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Kooperationsprojekt SFB 824 (3. Förderperiode) & BFSThe SFB824 (Sonderforschungsbereich 824: Central project for histopathology, immunohistochemistry and analytical microscopy) represents an interdisciplinary consortium which aims at the development of novel imaging technologies for the selection and monitoring of cancer therapy as an important support for personalized medicine. Z2, the central unit for comparative morphomolecular pathology and computational validation, provides integration, registration and quantification of data obtained from both macroscopic and (sub-)cellular in-vivo as well as ex-vivo imaging modalities with tissue-based morphomolecular readouts as the basis for the development and establishment of personalized medicine. In order to develop novel imaging technologies, co-annotation and validation of image data acquired by preclinical or diagnostic imaging platforms via tissue based quantitative morphomolecular methods is crucial. Light sheet microscopy will continue to close the gap between 3D data acquired by in-vivo imaging and 2D histological slices especially focusing on tumor vascularization. The Multimodal ImagiNg Data Flow StUdy Lab (MINDFUL) is a central system for data management in preclinical studies developed within SFB824. Continuing the close collaboration of pathology, computer sciences and basic as well as translational researchers from SFB824 will allow the Z2 to develop and subsequently provide a broad variety of registration and analysis tools for joint imaging and tissue based image standardization and quantification.The goal of the BFS Project: ImmunoProfiling using Neuronal Networks (IPN2) is to develop a method based on neuronal networks and recent advances in Deep Learning to allow characterization of a patient's tumor as ″hot″ or ″cold″ tumor depending on the identified ImmunoProfile. Recent research has shown that many tumors are infiltrated by immuno-competent cells, as well as that the amount, type and location of the infiltrated lymph nodes in primary tumors provide valuable prognostic information. In contrast to a ″cold tumor″, a ″hot tumor″ is characterized by an active immune system which the tumor has identified as threat. This identification provides the basis for selecting the therapy best suitable for the individual patient. |
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Developing Image Processing Techniques for Cryo Electron TomographyCryo-ET has recently emerged as a leading technique to investigate the three-dimensional(3D) structure of biological specimens at close-to-native state. It also bridges the gap between low-resolution imaging techniques (e.g. light microscopy) and high-resolution techniques (e.g. single particle electron microscopy). The technique consists of acquiring many two-dimensional (2D) projections of the structure under scrutiny at various tilt angles under cryogenic conditions. The 3D structure is recovered through a number of steps including projection alignment, reconstruction and de-nosing. The resolution of the reconstructed tomogram, however, is a affected by the low signal to noise ratio (SNR) of the projection images (typically 0.1 to 0.01) and the limited angular coverage (typically 60 to 70 ) resulting in wedge-shaped missing information in Fourier space, the so-called missing wedge; making the reconstruction process very challenging and demanding. |
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Stain Separation and Structure-Preserving Color Normalization for Histological ImagesStaining and scanning of tissue samples for microscopic examination is fraught with unwanted variations that affect their color appearance. Sources of these variations include differences in raw material and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. Color normalization of stained biopsies and tissue microarrays will help pathologists and computational pathology software while comparing different tissue samples. However, techniques that are used for natural images, such as histogram matching fail to utilize unique properties of stained tissue samples and produce undesirable artifacts. Tissue samples are stained with only a few reagents (frequently only two -- hemotoxylin and eosin or H\&E) and most tissue regions bind to only one stain or the other, thus producing sparse density maps composed of only a few components. This underlying structure of sparse stain density is biomedically important. We used these properties of stained tissue to propose a technique for stain separation and color normalization. Based on sparse non-negative matrix factorization (sparseNMF), we estimate prototype color and density map of each stain in an unsupervised manner to perform stain separation. To color normalize a given source image, we combine its stain density maps with the stain color prototypes of a target image whose appearance was preferred by pathologists. In this way, the normalized image preserve the biological structure encoded in the stain density of the source image. Both the proposed sparseNMF stain separation and color-normalization techniques yield higher correlation with ground truth than the state of the art. They are also rated qualitatively higher than other techniques by a group of pathologists. We further propose a computationally faster extension of this technique for large whole-slide images that selects an appropriately small sample of patches to compute the color prototypes of each stain instead of using the entire image. The fast scheme achieves a 20-folds acceleration, which does not only greatly enhance the analysis efficiency, but also allow its clinical applications to become practically feasible. |
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Microscopic Image Analysis |
Related Publications
| 2017 | |
| A. Keskin, M. Kekus, H. Adelsberger, U. Neumann, D. Shimshek, B. Song, B. Zott, T. Peng, H. Foerstl, M. Staufenbiel, I. Nelken, B. Sakmann, A. Konnerth, M. Busche
BACE inhibition-dependent repair of Alzheimer Ìs pathophysiology PNAS, in press (bib) |
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T. Peng, K. Thorn, T. Schroeder, L. Wang, F. Theis, C. Marr, N. Navab
A BaSiC? Tool for Background and Shading Correction of Optical Microscopy Images Nature Communications 8:14836 (bib) |
| G. Carneiro, T. Peng, C. Bayer, N. Navab
Automatic Quantification of Tumour Hypoxia from Multi-modal Microscopy Images using Weakly-Supervised Learning Methods IEEE Transactions on Medical Imaging PP(99):1-1 (bib) |
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| L. Peter, D. Mateus, P. Chatelain, D. Declara, N. Schworm, S. Stangl, G. Multhoff, N. Navab
Assisting the Examination of Large Histopathological Slides with Adaptive Forests Medical Image Analysis, Vol. 35, January 2017 (bib) |
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| 2016 | |
| G. Bortsova, M. Sterr, L. Wang, F. Milletari, N. Navab, A. Boettcher, H. Lickert, F. Theis, T. Peng
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields 7th International Workshop on Machine Learning in Medical Imaging (MLMI), Athena, Greece, October 2016 (bib) |
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| M. Mishra, S. Schmitt, L. Wang, M. Strasser, C. Marr, N. Navab, H. Zischka, T. Peng
Structure-based Assessment of Cancerous Mitochondria using Deep Networks International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, Prague, Czech Republic, April 2016 (bib) |
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| A. Vahadane, T. Peng, A. Sethi, S. Albarqouni, L. Wang, M. Baust, K. Steiger, A. M. Schlitter, I. Esposito, N. Navab
Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images IEEE Transactions on Medical Imaging (TMI), vol. 35, no. 8, pp. 1962 - 1971, 2016. (bib) |
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| 2015 | |
| G. Carneiro, T. Peng, C. Bayer, N. Navab
Weakly-supervised Structured Output Learning with Flexible and Latent Graphs using High-order Loss Functions International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015 (bib) |
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| G. Carneiro, T. Peng, C. Bayer, N. Navab
Automatic Detection of Necrosis, Normoxia and Hypoxia in Tumors from Multimodal Cytological Images International Conference on Image Processing (ICIP), Quebec, Canada, September 2015 (bib) |
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| G. Carneiro, T. Peng, C. Bayer, N. Navab
Flexible and Latent Structured Output Learning: Application to Histology. MICCAI Workshop Machine Learning in Medical Imaging (MLMI), Munich, Germany, September 2015 (bib) |
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| A. Vahadane, T. Peng, S. Albarqouni, M. Baust, K. Steiger, A. M. Schlitter, A. Sethi, I. Esposito, N. Navab
Structure-Preserved Color Normalization for Histological Images International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, New York, USA, April 2015 (bib) |
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| 2014 | |
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L. Peter, D. Mateus, P. Chatelain, N. Schworm, S. Stangl, G. Multhoff, N. Navab
Leveraging Random Forests for Interactive Exploration of Large Histological Images Proceedings of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, Massachusetts, United States, September 2014 (bib) |
| T. Peng, L. Wang, C. Bayer, S. Conjeti, M. Baust, N. Navab
Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Boston, USA, September 2014 (bib) |
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| M. Müller, M. Yigitsoy, H. Heibel, N. Navab
Deformable Reconstruction of Histology Sections using Structural Probability Maps International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Boston, USA, September 2014 (bib) |
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| T. Peng, M. Yigitsoy, A. Eslami, C. Bayer, N. Navab
Deformable registration of multi-modal microscopic images using a pyramidal interactive registration- learning methodology 6th Workshop on Biomedical Image Registration, London, UK, July, 2014 (bib) |
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| 2013 | |
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M. Yigitsoy, N. Navab
Structure Propagation for Image Registration IEEE Trans. Med. Imag., vol. 32, no. 9, pp. 1657-1670, September 2013 (bib) |
| 2012 | |
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L. Peter, O. Pauly, S. Jansen, P. Smethurst, W. Ouwehand
, N. Navab
Automatic Event Detection within Thrombus Formation Based on Binary Integer Programming MICCAI Workshop on Medical Computer Vision, Nice, France, October 2012 (bib) |
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L. Peter, N. Brieu, S. Jansen, P. Smethurst, W. Ouwehand
, N. Navab
Automatic Segmentation and Tracking of Thrombus Formation within In Vitro Microscopic Video Sequences IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2012), Barcelona, Spain, May 2 - 5, 2012 (bib) |
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N. Brieu, N. Navab, J. Serbanovic-Canic, W. Ouwehand
, D. Stemple, A. Cvejic, M. Groher
Image-based Characterization of Thrombus Formation in Time-lapse DIC Microscopy Medical Image Analysis 2012 (bib) |
| 2011 | |
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N. Brieu, M. Groher, J. Serbanovic-Canic, A. Cvejic, W. Ouwehand
, N. Navab
Joint Thrombus and Vessel Segmentation Using Dynamic Texture Likelihoods and Shape Prior Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, September 2011 (bib) |
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M. Feuerstein, H. Heibel, J. Gardiazabal, N. Navab, M. Groher
Reconstruction of 3-D Histology Images by Simultaneous Deformable Registration Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, September 2011. The original publication is available online at www.springerlink.com (bib) |
Working Group
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