Segmentation Techniques

Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint

“…Instance segmentation is sometimes cast as a combination of object detection and segmentation, in that a pixel-accurate segmentation of individual objects is attempted. This last concept corresponds best to previous work aimed at locating diatoms in digital images, although much of that works preceded the current terminology (13)(14)(15)(16)(17). Thus far, however, these methods have not been applied to gigapixel-sized virtual slide images, but usually to images taken manually and centering on individual diatom frustules.…”

“…This has led to extensive research on digitalization and automation of the light microscopy workflow (4,5). The main methodological steps in this context include large-scale automated light microscopic image acquisition at high optical resolution (6)(7)(8)(9)(10)(11)(12); localization of diatom frustules / valves in images (13)(14)(15)(16)(17)(18)(19); extraction of morphometric descriptors from image patches . CC-BY 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”

“…This has led to extensive research on digitalization and automation of the light microscopy workflow (4,5). The main methodological steps in this context include large-scale automated light microscopic image acquisition at high optical resolution (6)(7)(8)(9)(10)(11)(12); localization of diatom frustules / valves in images (13)(14)(15)(16)(17)(18)(19); extraction of morphometric descriptors from image patches depicting diatom frustules / valves (12,13,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30); and taxonomic identification, i.e., labelling of such image patches with a taxonomic name (26,(31)(32)(33). One of the most promising approaches towards large-scale light microscopic image acquisition from standard diatom microscopy slides is high-resolution slide scanning.…”

Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure

“…While the classification accuracy is excellent, these approaches rely on traditional image segmentation methods, such as Otsu’s thresholding ,, or region growing segmentation, to crop images to single diatom instances. For large images of complex environments, independent of artifacts, like color and contrast variations, noise, blurring, and impurities, a robust diatom detection is required.…”