Principled Design and Implementation of Steerable Detectors

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown position and orientation. We propose a continuous-domain additive image model, where the analyzed image is the sum of the template and an isotropic background signal with self-similar isotropic power-spectrum. The method is able to learn an optimal steerable filter fulfilling the SNR criterion based on one single template and background pair, that therefore strongly responds to the template, while optimally decoupling from the background model. The proposed filter then allows for a fast detection process, with the unknown orientation estimation through the use of steerability properties. In practice, the implementation requires to discretize the continuous-domain formulation on polar grids, which is performed using radial B-splines. We demonstrate the practical usefulness of our method on a variety of template approximation and pattern detection experiments

Made to measure: An introduction to quantifying microscopy data in the life sciences

Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments

µMatch: 3D shape correspondence for biological image data

Modern microscopy technologies allow imaging biological objects in 3D over a wide range of spatial and temporal scales, opening the way for a quantitative assessment of morphology. However, establishing a correspondence between objects to be compared, a first necessary step of most shape analysis workflows, remains challenging for soft-tissue objects without striking features allowing them to be landmarked. To address this issue, we introduce the μMatch 3D shape correspondence pipeline. μMatch implements a state-of-the-art correspondence algorithm initially developed for computer graphics and packages it in a streamlined pipeline including tools to carry out all steps from input data pre-processing to classical shape analysis routines. Importantly, μMatch does not require any landmarks on the object surface and establishes correspondence in a fully automated manner. Our open-source method is implemented in Python and can be used to process collections of objects described as triangular meshes. We quantitatively assess the validity of μMatch relying on a well-known benchmark dataset and further demonstrate its reliability by reproducing published results previously obtained through manual landmarking

A biologist’s guide to planning and performing quantitative bioimaging experiments

Technological advancements in biology and microscopy have empowered a transition from bioimaging as an observational method to a quantitative one. However, as biologists are adopting quantitative bioimaging and these experiments become more complex, researchers need additional expertise to carry out this work in a rigorous and reproducible manner. This Essay provides a navigational guide for experimental biologists to aid understanding of quantitative bioimaging from sample preparation through to image acquisition, image analysis, and data interpretation. We discuss the interconnectedness of these steps, and for each, we provide general recommendations, key questions to consider, and links to high-quality open-access resources for further learning. This synthesis of information will empower biologists to plan and execute rigorous quantitative bioimaging experiments efficiently

Landmark Active Contours for Bioimage Analysis:A Tale of Points and Curves

The problem of identifying the outline of objects in images can be approached from two starting points, either by considering localized features (landmarks, keypoints or regions), or by searching for global contours. Features are regions or points of interest and usually include a description of the local properties of the image around them. The definition of a feature is flexible. Most often, it consists of a list of desirable properties inspired by the application at hand. Algorithms are then designed to robustly detect occurrences of the feature in the image under the effect of various geometrical transformations. Contours, on the other hand, are (portions of) curves that can be delineated using deformable models, for instance relying on spline curves. Splines are in particular at the core of a large family of such models called spline-based active contours, or designer snakes. These methods can be customized and adapted to outline a large variety of objects in many types of images. In this thesis, we aim at unifying these two strategies by bridging automated feature detection and spline-based active contour segmentation for bioimage analysis. Our work proceeds in three steps. First, we introduce and characterize the Hermite spline interpolation framework, a model that allows incorporating local information at each node in the spline curve. Then, we study the design of custom feature detectors based on the steerable filters formalism. With these two ingredients, we propose a semiautomated segmentation algorithm called the landmark snake, which is defined relying on Hermite interpolation and evolves a curve in the image to outline objects of interest using information provided by steerable features detectors. The Hermite spline formalism allows for a direct correspondence between control points on the spline curve and landmarks, simplifying the algorithm design and allowing for user-friendly interactions. The approach is generic enough to be used in a wide variety of data, as will be illustrated through real bioimage analysis problems in the context of collaborative work with external laboratories