Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020191145

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Organ printing can be defined as layer-by-layer additive robotic biofabrication of three-dimensional functional living macrotissues and organ constructs using tissue spheroids as building blocks. The microtissues and tissue spheroids are living materials with certain measurable, evolving and potentially controllable composition, material and biological properties. Closely placed tissue spheroids undergo tissue fusion - a process that represents a fundamental biological and biophysical principle of developmental biology-inspired directed tissue self-assembly. It is possible to engineer small segments of an intraorgan branched vascular tree by using solid and lumenized vascular tissue spheroids. Organ printing could dramatically enhance and transform the field of tissue engineering by enabling large-scale industrial robotic biofabrication of living human organ constructs with "built-in" perfusable intraorgan branched vascular tree. Thus, organ printing is a new emerging enabling technology paradigm which represents a developmental biology-inspired alternative to classic biodegradable solid scaffold-based approaches in tissue engineering.

Organ Printing: Tissue Spheroids as Building Blocks

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), uses the viral spike (S) protein for host cell attachment and entry. The host protease furin cleaves the full-length precursor S glycoprotein into two associated polypeptides: S1 and S2. Cleavage of S generates a polybasic Arg-Arg-Ala-Arg carboxyl-terminal sequence on S1, which conforms to a C-end rule (CendR) motif that binds to cell surface neuropilin-1 (NRP1) and NRP2 receptors. We used x-ray crystallography and biochemical approaches to show that the S1 CendR motif directly bound NRP1. Blocking this interaction by RNA interference or selective inhibitors reduced SARS-CoV-2 entry and infectivity in cell culture. NRP1 thus serves as a host factor for SARS-CoV-2 infection and may potentially provide a therapeutic target for COVID-19.

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Neuropilin-1 is a host factor for SARS-CoV-2 infection.

The potential of a spheroid tumor model composed of cells in different proliferative and metabolic states for the development of new anticancer strategies has been amply demonstrated. However, there is little or no information in the literature on the problems of reproducibility of data originating from experiments using 3D models. Our analyses, carried out using a novel open source software capable of performing an automatic image analysis of 3D tumor colonies, showed that a number of morphology parameters affect the response of large spheroids to treatment. In particular, we found that both spheroid volume and shape may be a source of variability. We also compared some commercially available viability assays specifically designed for 3D models. In conclusion, our data indicate the need for a pre-selection of tumor spheroids of homogeneous volume and shape to reduce data variability to a minimum before use in a cytotoxicity test. In addition, we identified and validated a cytotoxicity test capable of providing meaningful data on the damage induced in large tumor spheroids of up to diameter in 650 μm by different kinds of treatments.

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3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained

Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.

Deep learning for cellular image analysis

Uneven illumination affects every image acquired by a microscope. It is often overlooked, but it can introduce considerable bias to image measurements. The most reliable correction methods require special reference images, and retrospective alternatives do not fully model the correction process. Our approach overcomes these issues for most optical microscopy applications without the need for reference images.

https://www.zora.uzh.ch/id/eprint/112219/1/Smith_BC21361B_revision4.pdf

CIDRE: an illumination-correction method for optical microscopy

Motivation Time-lapse experiments play a key role in studying the dynamic behavior of cells. Single-cell tracking is one of the fundamental tools for such analyses. The vast majority of the recently introduced cell tracking methods are limited to fluorescently labeled cells. An equally important limitation is that most software cannot be effectively used by biologists without reasonable expertise in image processing. Here we present CellTracker, a user-friendly open-source software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast and differential interference contrast (DIC) techniques. Availability and implementation CellTracker is written in MATLAB (The MathWorks, Inc., USA). It works with Windows, Macintosh and UNIX-based systems. Source code and graphical user interface (GUI) are freely available at: http://celltracker.website/ Contact horvath.peter@brc.mta.hu Supplementary information Supplementary data are available at Bioinformatics online.

CellTracker (not only) for dummies

Motivation Microscopy images of stained cells and tissues play a central role in most biomedical experiments and routine histopathology. Storing colour histological images digitally opens the possibility to process numerically colour distribution and intensity to extract quantitative data. Among those numerical procedures is colour deconvolution, which enables decomposing an RGB image into channels representing the optical absorbance and transmittance of the dyes when their RGB representation is known. Consequently, a range of new applications become possible for morphological and histochemical segmentation, automated marker localisation and image enhancement. Availability and implementation Colour deconvolution is presented here in two open-source forms: a MATLAB program/function and an ImageJ plugin written in Java. Both versions run in Windows, Macintosh, and UNIX-based systems under the respective platforms. Source code and further documentation are available at: https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/. Supplementary information Supplementary data are available at Bioinformatics online.

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Colour deconvolution: stain unmixing in histological imaging

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

Filippo Piccinini

Papers

3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained

CIDRE: an illumination-correction method for optical microscopy

CellTracker (not only) for dummies

Colour deconvolution: stain unmixing in histological imaging

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays