No abstract

Background Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. Results We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. Conclusions We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided. Electronic supplementary material The online version of this article (10.1186/s12859-019-2880-8) contains supplementary material, which is available to authorized users.

A new signal model and processing method for quantitative ultrasound perfusion analysis is presented, called bolus-and-burst. The method has the potential to provide absolute values of blood flow, blood volume, and mean transit time. Furthermore, it provides an estimate of the local arterial input function which characterizes the arterial tree, allowing accurate estimation of the bolus arrival time. The method combines two approaches to ultrasound perfusion analysis: bolus-tracking and burst-replenishment. A pharmacokinetic model based on the concept of arterial input functions and tissue residue functions is used to model both the bolus and replenishment parts of the recording. The pharmacokinetic model is fitted to the data using blind deconvolution. A preliminary assessment of the new perfusion-analysis method is presented on clinical recordings.