<i>Sashimi</i>: A toolkit for facilitating high‐throughput organismal image segmentation using deep learning

Schwartz, Shawn T.; Alfaro, Michael E.

doi:10.1111/2041-210x.13712

Cited by 20 publications

(20 citation statements)

References 98 publications

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“…can also ignore a background of uniform color by specifying a range of RGB colors. There are several tools available for automatic background masking, such as Sashimi (Schwartz and Alfaro, 2021) or Batch-Mask (Curlis et al, 2021), which could be used for large image sets. The exception is for the workflows we show in examples E and o F below, where the landmark alignment performs automatic background masking on ι unmasked images.…”

Section: Resultsmentioning

confidence: 99%

Flexible color segmentation of biological images with the R package recolorize

Weller

Hiller

Belleghem

et al. 2022

Preprint

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Color patterns are easy to observe, but difficult to quantify. A necessary first step for most digital color pattern analysis methods is generating color maps, where every pixel in an image is assigned to one of a set of discrete color pattern elements (color segmentation). Most automatic methods for color segmentation rely on color value thresholds or k-means clustering. While these work in specialized cases, these methods often fail due to biological variation in pattern distribution and intensity, technical variation in images, and poor scalability for batch clustering. As a result, color segmentation is the bottleneck step in a wide variety of analyses. Here we present recolorize, an R package for color segmentation that succeeds in many cases where currently used methods fail. Recolorize has three major components: (1) an effective two-part algorithm for color clustering where color distributions are binned and combined according to perceived similarity; (2) a toolkit for making minor manual adjustments to automatic output where needed; and (3) easy export options to a variety of methods that require accurate color maps. This paper illustrates how to use recolorize, includes examples where we effectively address many of the problems that fail using k-means clustering , and combines the package with two of the most popular R packages for quantifying color pattern variation (patternize and pavo).

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Section: Resultsmentioning

confidence: 99%

Flexible color segmentation of biological images with the R package recolorize

Weller

Hiller

Belleghem

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…While for this purpose machine learning is typically not needed to achieve good segmentation, it moreover is often not even possible, because training data, sufficient time, or GPU hardware appropriate for deep learning are lacking (Lürig et al., 2021). For cases without these constraints, with very large datasets, or with high levels of noise, recent innovative Python machine learning toolkits like ml‐morph (Porto & Voje, 2020) or sashimi (Schwartz & Alfaro, 2021) are recommended alternatives.…”

Section: Discussion and Outlookmentioning

confidence: 99%

phenopype: A phenotyping pipeline for Python

Lürig

2021

Methods Ecol Evol

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“…While for this purpose machine learning is typically not needed to achieve good segmentation, it moreover is often not even possible, because training data, sufficient time, or GPU-hardware appropriate for deep learning are lacking . For cases without these constraints, with very large datasets, or with high levels of noise, recent innovative Python machine learning toolkits like ml-morph (Porto & Voje, 2020) or sashimi (Schwartz & Alfaro, 2021) are recommended alternatives.…”

Section: Discussion and Outlookmentioning

confidence: 99%

phenopype: a phenotyping pipeline for Python

Luerig

2021

Preprint

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Digital images are a ubiquitous way to represent phenotypes. More and more ecologists and evolutionary biologists are using images to capture and analyze high dimensional phenotypic data to understand complex developmental and evolutionary processes. As a consequence, images are being collected at ever increasing rates, already outpacing our abilities for processing and analysis of the contained phenotypic information.phenopype is a high throughput phenotyping package for the programming language Python to support ecologists and evolutionary biologists in extracting high dimensional phenotypic data from digital images. phenopype integrates existing state-of-the-art computer vision functions (using the OpenCV library as a backend), GUI-based interactions, and a project management ecosystem to facilitate rapid data collection and reproducibility.phenopype offers three different workflow types that support users during different stages of scientific image analysis (prototyping, low-throughput, and high-throughput). In the high-throughput workflow, users interact with human-readable YAML configuration files to effectively modify settings for different images. These settings are stored along with processed images and results, so that the acquired phenotypic information becomes highly reproducible.phenopype combines the advantages of the Python environment, with its state-of-the-art computer vision, array manipulation and data handling libraries, and basic GUI capabilities, which allow users to step into the automatic workflow when necessary. Overall, phenopype is aiming to augment, rather than replace the utility of existing Python CV libraries, allowing biologists to focus on rapid and reproducible data collection.

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Sashimi: A toolkit for facilitating high‐throughput organismal image segmentation using deep learning

Cited by 20 publications

References 98 publications

Flexible color segmentation of biological images with the R package recolorize

Flexible color segmentation of biological images with the R package recolorize

phenopype: A phenotyping pipeline for Python

phenopype: a phenotyping pipeline for Python

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