In order to train a classifier that is robust to the variation across these large image sets it is beneficial to create a training set consisting of smaller cropped images of randomly selected regions in these images. For our particular application, high resolution multichannel confocal fluorescence images of rodent brains were routinely 22,000 × 18,000 pixels or larger, and 5 or more images per animal need to be segmented to produce useful quantitative data. These limitations make it difficult to reproduce and update classifiers, to use alternative software and input devices for annotation, and to share training sets in a transparent and collaborative manner.įor biomedical microscopy, the advantage of machine learning image segmentation is the ability to apply a single classifier to large image sets which vary somewhat in brightness, background level and other image attributes. Although Ilastik and Trainable Segmentation have limited support for importing and exporting image annotations, neither supports both batch import and batch export of these annotations in standard formats. After training, a classifier can be saved and used to perform objective and repeatable segmentation of large numbers of similarly processed images. The context of each pixel (e.g., intensity, texture, edges, entropy) can be considered, making the classifier more robust to image artifacts and intensity shifts. Open-source trainable segmentation tools, such as Ilastik or the Trainable Segmentation plugin for ImageJ, address many of these issues by using supervised machine learning algorithms to study ‘training set’ of pixels, which have been manually assigned class annotations, and create a model (‘classifier’) to reliably discriminate between these classes. This complicates comparisons between studies and poses replication problems, as legacy software and hardware may no longer be available, and algorithms or interfaces may differ between versions. ![]() When automated segmentation tools are used, they are often commercial platforms whose detailed algorithms are proprietary. ![]() In manual segmentation, parameter choice is influenced by conditions such as screen brightness and dynamic range, ambient light, perceived brightness, and subjective bias especially in unblinded raters, and such factors are rarely reported, limiting reproducibility. The use of image segmentation in research raises several reproducibility issues. Parameters or seeds for segmentation are often manually selected on a per-image basis based on a preview of the result, or may be selected, reviewed and refined in an unstructured iterative process. Many segmentation techniques such as thresholding, where pixels above a selected intensity threshold are discriminated from background, depend exclusively on the brightness of individual pixels, making them sensitive to noise and regional variations in intensity. ImageJ sample image leaf.jpeg (c) and segmented image (d). ![]() Confocal fluorescence image of Iba-1 labelled microglial cells in APPswe/PS1dE9 mouse brain tissue (a) and segmented image (b).
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