Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers

Advances in allergen-microarray technology for diagnosis and monitoring of allergy: The MeDALL allergen-chip

Allergy diagnosis based on purified allergen molecules provides detailed information regarding the individual sensitization profile of allergic patients, allows monitoring of the development of allergic disease and of the effect of therapies on the immune response to individual allergen molecules. Allergen microarrays contain a large variety of allergen molecules and thus allow the simultaneous detection of allergic patients' antibody reactivity profiles towards each of the allergen molecules with only minute amounts of serum. In this article we summarize recent progress in the field of allergen microarray technology and introduce the MeDALL allergen-chip which has been developed for the specific and sensitive monitoring of IgE and IgG reactivity profiles towards more than 170 allergen molecules in sera collected in European birth cohorts. MeDALL is a European research program in which allergen microarray technology is used for the monitoring of the development of allergic disease in childhood, to draw a geographic map of the recognition of clinically relevant allergens in different populations and to establish reactivity profiles which are associated with and predict certain disease manifestations. We describe technical advances of the MeDALL allergen-chip regarding specificity, sensitivity and its ability to deliver test results which are close to in vivo reactivity. In addition, the usefulness and numerous advantages of allergen microarrays for allergy research, refined allergy diagnosis, monitoring of disease, of the effects of therapies, for improving the prescription of specific immunotherapy and for prevention are discussed.This work was supported by the European Commission’s Seventh Framework Programme under grant agreement No. 261357 and in part by project F4605 of the Austrian Science Fund (FWF) and the Christian Doppler Research Association, Austri

Bioconda: A sustainable and comprehensive software distribution for the life sciences

We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges