Abstract

With the progress of microscopy techniques and the rapidly growing amounts of acquired imaging data, there is an increased need for automated image processing and analysis solutions in biological studies. Each new application requires the design of a specific image analysis pipeline, by assembling a series of image processing operations. Many commercial or free bioimage analysis software are now available and several textbooks and reviews have presented the mathematical and computational fundamentals of image processing and analysis. Tens, if not hundreds, of algorithms and methods have been developed and integrated into image analysis software, resulting in a combinatorial explosion of possible image processing sequences. This paper presents a general guideline methodology to rationally address the design of image processing and analysis pipelines. The originality of the proposed approach is to follow an iterative, backwards procedure from the target objectives of analysis. The proposed goal-oriented strategy should help biologists to better apprehend image analysis in the context of their research and should allow them to efficiently interact with image processing specialists.

Abstract

Root system analysis is a complex task, often performed using fully automated image analysis pipelines. However, these pipelines are usually evaluated with a limited number of ground-truth root images, most likely of limited size and complexity. We have used a root model, ArchiSimple to create a large and diverse library of ground-truth root system images (10.000). This library was used to evaluate the accuracy and usefulness of several image descriptors classicaly used in root image analysis pipelines. Our analysis highlighted that the accuracy of the different metrics is strongly linked to the type of root system analyzed (e.g. dicot or monocot) as well as their size and complexity. Metrics that have been shown to be accurate for small dicot root systems might fail for large dicots root systems or small monocot root systems. Our study also demonstrated that the usefulness of the different metrics when trying to discriminate genotypes or experimental conditions may vary. Overall, our analysis is a call to caution when automatically analyzing root images. If a thorough calibration is not performed on the dataset of interest, unexpected errors might arise, especially for large and complex root images. To facilitate such calibration, both the image library and the different codes used in the study have been made available to the community. Document type: Preprint