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Reconstruction of shapes, forms, and sizes of three-dimensional (3D) objects from two-dimensional (2D) information is one of the most complex functions of the human brain. It also poses an algorithmic challenge and at present is a widely studied subject in computer vision. We here focus on the single cell level and present a neural network-based SHApe PRediction autoencoder SHAPR that accurately reconstructs 3D cellular and nuclear shapes from 2D microscopic images and may have great potential for application in the biomedical sciences.

AbstractMotivationDeep learning contributes to uncovering and understanding molecular and cellular processes with highly performant image computing algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate, consistent and fast data processing. However, published algorithms mostly solve only one specific problem and they often require expert skills and a considerable computer science and machine learning background for application.ResultsWe have thus developed a deep learning pipeline called InstantDL for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables experts and non-experts to apply state-of-the-art deep learning algorithms to biomedical image data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows to assess the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible.Availability and ImplementationInstantDL is available under the terms of MIT licence. It can be found on GitHub: https://github.com/marrlab/InstantDLContactcarsten.marr@helmholtz-muenchen.de

Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

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Objective and Impact Statement: We apply a deep learning (DL) segmentation method and automate the extraction of imaging markers for neonatal lung structure using magnetic resonance imaging (MRI) in order to inform clinical care with robust and quantifiable information about the neonatal lung. Introduction: Quantification of lung structural information in a standardized fashion is crucial to inform diagnostic processes that enable personalized treatment and monitoring strategies. Increased efficiency and accuracy in image quantification is especially needed in prematurely born infants, for whom long-term survival is critically determined by acute and chronic pulmonary complications, currently diagnosed based on clinical criteria due to the lack of routinely applicable diagnostic tools. Methods: We prospectively enrolled 107 premature infants in two clinical centers with and without chronic lung disease, i.e., Bronchopulmonary Dysplasia (BPD) to perform quiet-breathing lung MRI. An ensemble of deep convolutional neural networks was developed to perform lung segmentation, with a subsequent reconstruction of the 3-dimensional lung and computation of MRI volumetric measurements and compared to the standard manual segmentation. Results: The DL model successfully annotates lung segments with a volumetric dice score of 0.908 (Site 1) and 0.880 (Site 2), thereby reaching expert-level performance while demonstrating high transferability between study sites and robustness towards technical (low spatial resolution, movement artifacts) and disease conditions. Estimated lung volumes correlated with infant lung function testing measures and enabled the separation of neonates with and without BPD. Conclusion: Our work demonstrates the potential of AI-supported MRI measures to perform monitoring of neonatal lung development and characterization of respiratory diseases in this high-risk patient cohort.