CHROMAS: A Computational Pipeline to Track Chromatophores and Analyze their Dynamics

Reviewer #1 (Public review):

Summary:

This study provides comprehensive instructions for using the chromatophore tracking software, Chromas, to track and analyse the dynamics of large numbers of cephalopod chromatophores across various spatiotemporal scales. This software addresses a long-standing challenge faced by many researchers who study these soft-bodied creatures, known for their remarkable ability to change colour rapidly. The updated software features a user-friendly interface that can be applied to a wide range of applications, making it an essential tool for biologists focused on animal dynamic signalling. It will also be of interest to professionals in the fields of computer vision and image analysis.

Strengths:

This work provides detailed instructions for this toolkit along with examples for potential users to try. The Gitlab inventory hosts the software package, installation documentation, and tutorials, further helping potential users with a less steep learning curve.

Weaknesses:

The evidence supporting the authors' claims is solid, particularly demonstrated through the use of cuttlefish and squid. However, it may not be applicable to all coleoid cephalopods yet, such as octopuses, which have an incredibly versatile ability to change their body forms.

Reviewer #2 (Public review):

Summary:

The authors developed a computational pipeline named CHROMAS to track and analyze chromatophore dynamics, which provides a wide range of biological analysis tools without requiring the user to write code.

Strengths:

(1) CHROMAS is an integrated toolbox that provides tools for different biological tasks such as: segment, classify, track and measure individual chromatophores, cluster small groups of chromatophores, analyze full-body patterns, etc.

(2) It could be used to investigate different species. The authors have already applied it to analyze the skin of the bobtail squid Euprymna berryi and the European cuttlefish Sepia officinalis.

(3) The tool is open-source and easy to install. The paper describes in detail the command format to complete each task and provides relevant sample figures.

Weaknesses:

(1) The generality and robustness of the proposed pipeline need to be verified through more experimental evaluations. For example, the implementation algorithm depends on relatively specific or obvious image features, clean backgrounds, and objects that do not move too fast.

(2) The pipeline lacks some kind of self-correction mechanism. If at one moment there is a conflicting match with the previous frames, how does the system automatically handle it to ensure that the tracking results are accurate over a long period of time?