LLAMA: a robust and scalable machine learning pipeline for analysis of cell surface projections in large scale 4D microscopy data

Abstract

We present LLAMA, a pipeline for systematic analysis of terabyte scale 4D microscopy datasets. Analysis of individual biological structures in imaging at this scale requires efficient and robust methods which do not require human micromanagement or editing of outputs. To meet this challenge, we use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, and the generation of detailed object level statistics. Advanced visualisation is a key element of LLAMA: we provide a specialised software tool which supports quality control and optimisation as well as visualisation of outputs. LLAMA was used in a quantitative analysis of macrophage surface membrane projections (filopodia, ruffles, tent-pole ruffles) examining the differential effects of two interventions: lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1). Distinct patterns of increased activity were identified. In addition, a continuity of behaviour was found between tent pole ruffling and wave-like ruffling, further defining the role of filopodia in ruffling.

Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History

“…As new restoration intervention technologies are developed, further improvements will be made to conservation tools. For example, although this analysis used 2D images, this pipeline is also capable of processing 3D images [34]. This will deliver estimates of the size and color of 3D scanned corals, further improving biological information, throughput, time efficiency, accuracy, and ease of use in the field of current 3D methods [34,[73][74][75] and apply them to early life stages, which are especially challenging for small coral juveniles (see Quigley et al [19]).…”

“…For example, although this analysis used 2D images, this pipeline is also capable of processing 3D images [34]. This will deliver estimates of the size and color of 3D scanned corals, further improving biological information, throughput, time efficiency, accuracy, and ease of use in the field of current 3D methods [34,[73][74][75] and apply them to early life stages, which are especially challenging for small coral juveniles (see Quigley et al [19]). Understanding coral ontogeny over all life stages is essential, in which multiple methods are needed to understand growth form and structural complexity of corals.…”