Computational Pathology

@article{fuchs_computational_2011,
    title = {Computational {Pathology}: {Challenges} and {Promises} for {Tissue} {Analysis}},
    volume = {35},
    issn = {0895-6111},
    shorttitle = {Computational pathology},
    url = {http://www.sciencedirect.com/science/article/pii/S0895611111000383},
    doi = {10.1016/j.compmedimag.2011.02.006},
    abstract = {The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.},
    number = {7–8},
    urldate = {2012-03-08},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2011},
    keywords = {Cancer research, Computational pathology, Medical imaging, Survival statistics, Whole, Whole slide imaging, imaging, machine learning, slide},
    pages = {515--530},
}

@inproceedings{schueffler_tmarker:_2013,
    title = {{TMARKER}: {A} {Robust} and {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Immunohistochemical} {Staining} {Estimations}},
    url = {http://link.springer.com/article/10.1007%2Fs00292-013-1765-2},
    abstract = {Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
    Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
    Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
    Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.},
    booktitle = {German {Society} of {Pathology} 97th {Annual} {Meeting}},
    author = {Schueffler, Peter J. and Rupp, Niels and Ong, Cheng S. and Buhmann, Joachim M. and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2013},
}

@incollection{schueffler_automated_2013,
    address = {London},
    series = {Advances in {Computer} {Vision} and {Pattern} {Recognition}},
    title = {Automated {Analysis} of {Tissue} {Micro}-{Array} {Images} on the {Example} of {Renal} {Cell} {Carcinoma}},
    isbn = {978-1-4471-5627-7},
    url = {https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7},
    abstract = {Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.},
    booktitle = {Similarity-{Based} {Pattern} {Analysis} and {Recognition}},
    publisher = {Springer},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2013},
    pages = {219--246},
}

@article{meyer_seven-marker_2012,
    title = {A {Seven}-{Marker} {Signature} and {Clinical} {Outcome} in {Malignant} {Melanoma}: {A} {Large}-{Scale} {Tissue}-{Microarray} {Study} with {Two} {Independent} {Patient} {Cohorts}},
    volume = {7},
    shorttitle = {A {Seven}-{Marker} {Signature} and {Clinical} {Outcome} in {Malignant} {Melanoma}},
    url = {http://dx.doi.org/10.1371/journal.pone.0038222},
    doi = {10.1371/journal.pone.0038222},
    abstract = {BackgroundCurrent staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment.Methods and FindingsUsing tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications.ConclusionsThe seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.},
    number = {6},
    urldate = {2016-01-03},
    journal = {PLoS ONE},
    author = {Meyer, Stefanie and Fuchs, Thomas J. and Bosserhoff, Anja K. and Hofst\"adter, Ferdinand and Pauer, Armin and Roth, Volker and Buhmann, Joachim M. and Moll, Ingrid and Anagnostou, Nikos and Brandner, Johanna M. and Ikenberg, Kristian and Moch, Holger and Landthaler, Michael and Vogt, Thomas and Wild, Peter J.},
    year = {2012},
    pages = {e38222},
}

@inproceedings{schueffler_tmarker:_2012,
    title = {{TMARKER}: {A} {User}-{Friendly} {Open}-{Source} {Assistance} for {Tma} {Grading} and {Cell} {Counting}},
    booktitle = {Histopathology {Image} {Analysis} ({HIMA}) {Workshop} at the 15th {International} {Conference} on {Medical} {Image} {Computing} and {Computer} {Assisted} {Intervention} {MICCAI}},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2012},
}

@article{haybaeck_aerosols_2011,
    title = {Aerosols {Transmit} {Prions} to {Immunocompetent} and {Immunodeficient} {Mice}},
    volume = {7},
    url = {http://dx.doi.org/10.1371/journal.ppat.1001257},
    doi = {10.1371/journal.ppat.1001257},
    abstract = {Author Summary
    Prions, which are the cause of fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs), can be experimentally or naturally transmitted via prion-contaminated food, blood, milk, saliva, feces and urine. Here we demonstrate that prions can be transmitted through aerosols in mice. This also occurs in the absence of immune cells as demonstrated by experiments with mice lacking B-, T-, follicular dendritic cells (FDCs), lymphotoxin signaling or with complement-deficient mice. Therefore, a functionally intact immune system is not strictly needed for aerogenic prion infection. These results suggest that current biosafety guidelines applied in diagnostic and scientific laboratories ought to include prion aerosols as a potential vector for prion infection.},
    number = {1},
    urldate = {2016-01-03},
    journal = {PLoS Pathog},
    author = {Haybaeck, Johannes and Heikenwalder, Mathias and Klevenz, Britta and Schwarz, Petra and Margalith, Ilan and Bridel, Claire and Mertz, Kirsten and Zirdum, Elizabeta and Petsch, Benjamin and Fuchs, Thomas J. and Stitz, Lothar and Aguzzi, Adriano},
    year = {2011},
    pages = {e1001257},
}

@inproceedings{schueffler_computational_2010,
    address = {Darmstadt, Germany},
    title = {Computational {TMA} {Analysis} and {Cell} {Nucleus} {Classification} of {Renal} {Cell} {Carcinoma}},
    isbn = {3-642-15985-0 978-3-642-15985-5},
    url = {http://portal.acm.org/citation.cfm?id=1926258.1926281},
    doi = {10.1007/978-3-642-15986-2_21},
    abstract = {We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.},
    booktitle = {Proceedings of the 32nd {DAGM} conference on {Pattern} recognition},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2010},
    pages = {202--211},
}

@article{bettermann_tak1_2010,
    title = {{TAK1} {Suppresses} a {NEMO}-{Dependent} but {NF}-{kappaB}-{Independent} {Pathway} to {Liver} {Cancer}},
    volume = {17},
    url = {http://www.ncbi.nlm.nih.gov/pubmed/20478530},
    number = {5},
    journal = {Cancer Cell},
    author = {Bettermann, Kira and Vucur, Mihael and Haybaeck, Johannes and Koppe, Christiane and Janssen, Joern and Heymann, Felix and Weber, Achim and Weiskirchen, Ralf and Liedtke, Christian and Gassler, Nikolaus and Mueller, Michael and de Vos, Rita and Wolf, Monika Julia and Boege, Yannick and Seleznik, Gitta Maria and Zeller, Nicolas and Erny, Daniel and Fuchs, Thomas J. and Zoller, Stefan and Cairo, Stefano and Buendia, Marie-Annick and Prinz, Marco and Akira, Shizuo and Tacke, Frank and Heikenwaelder, Mathias and Trautwein, Christian and Luedde, Tom},
    year = {2010},
    pages = {481--496},
}

@article{raman_infinite_2010,
    title = {Infinite {Mixture}-of-{Experts} {Model} for {Sparse} {Survival} {Regression} with {Application} to {Breast} {Cancer}},
    volume = {11},
    issn = {1471-2105},
    url = {http://dx.doi.org/10.1186/1471-2105-11-S8-S8},
    doi = {10.1186/1471-2105-11-S8-S8},
    abstract = {We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.},
    number = {8},
    urldate = {2016-01-03},
    journal = {BMC Bioinformatics},
    author = {Raman, Sudhir and Fuchs, Thomas J. and Wild, Peter J. and Dahl, Edgar and Buhmann, Joachim M. and Roth, Volker},
    year = {2010},
    pages = {S8},
}

@inproceedings{fuchs_randomized_2009,
    address = {Las Vegas, Nevada},
    series = {{ISVC} '09},
    title = {Randomized {Tree} {Ensembles} for {Object} {Detection} in {Computational} {Pathology}},
    isbn = {978-3-642-10330-8},
    url = {http://dx.doi.org/10.1007/978-3-642-10331-5_35},
    doi = {http://dx.doi.org/10.1007/978-3-642-10331-5_35},
    booktitle = {Proceedings of the 5th {International} {Symposium} on {Advances} in {Visual} {Computing}: {Part} {I}},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Fuchs, Thomas J. and Haybaeck, Johannes and Wild, Peter J. and Heikenwalder, Mathias and Moch, Holger and Aguzzi, Adriano and Buhmann, Joachim M.},
    year = {2009},
    pages = {367--378},
}

@inproceedings{fuchs_inter-active_2009,
    title = {Inter-{Active} {Learning} of {Randomized} {Tree} {Ensembles} for {Object} {Detection}},
    isbn = {978-1-4244-4442-7},
    url = {http://dx.doi.org/10.1109/ICCVW.2009.5457452},
    doi = {http://dx.doi.org/10.1109/ICCVW.2009.5457452},
    booktitle = {Computer {Vision} {Workshops} ({ICCV} {Workshops}), 2009 {IEEE} 12th {International} {Conference} on {Computer} {Vision}},
    author = {Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2009},
    pages = {1370--1377},
}

@inproceedings{floros_graph-based_2009,
    title = {Graph-{Based} {Pancreatic} {Islet} {Segmentation} for {Early} {Type} 2 {Diabetes} {Mellitus} on {Histopathological} {Tissue}},
    url = {http://www.springerlink.com/index/2PG4628N77772L77.pdf},
    booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} {MICCAI}},
    author = {Floros, Xenofon E. and Fuchs, Thomas J. and Rechsteiner, Markus P. and Spinas, Giatgen and Moch, Holger and Buhmann, Joachim M.},
    year = {2009},
    pages = {633--640},
}

@inproceedings{fuchs_weakly_2008,
    address = {Berlin, Heidelberg},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Weakly {Supervised} {Cell} {Nuclei} {Detection} and {Segmentation} on {Tissue} {Microarrays} of {Renal} {Cell} {Carcinoma}},
    volume = {5096},
    isbn = {978-3-540-69320-8},
    url = {http://www.springerlink.com/index/L380G56T1V230715.pdf},
    doi = {10.1007/978-3-540-69321-},
    booktitle = {Pattern {Recognition}. {DAGM} 2008},
    publisher = {Springer-Verlag},
    author = {Fuchs, Thomas J. and Lange, Tilman and Wild, Peter J. and Moch, Holger and Buhmann, Joachim M.},
    year = {2008},
    pages = {173--182},
}

@inproceedings{fuchs_computational_2008,
    address = {Berlin, Heidelberg},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Computational {Pathology} {Analysis} of {Tissue} {Microarrays} {Predicts} {Survival} of {Renal} {Clear} {Cell} {Carcinoma} {Patients}},
    volume = {5242},
    isbn = {978-3-540-85989-5},
    url = {http://dx.doi.org/10.1007/978-3-540-85990-1_1},
    doi = {10.1007/978-3-540-85990-1_1},
    booktitle = {Proceedings of the international conference on {Medical} {Image} {Computing} and {Computer}-{Assisted} {Intervention} {MICCAI}},
    publisher = {Springer-Verlag},
    author = {Fuchs, Thomas J. and Wild, Peter J. and Moch, Holger and Buhmann, Joachim M.},
    year = {2008},
    keywords = {Computer Science},
    pages = {1--8},
}