Ilastik Carving
Ilastik Carving - Related Videos
05:25
Quantifying Drosophila melanogaster Eye Phenotypes: A Computational Approach Integrating ilastik and Flynotyper
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2024
The Drosophila eye system is a useful tool for studying various biological processes, particularly human neurodegenerative diseases. However, manual quantification of rough eye phenotypes can be biased and unreliable. Here we describe a method by which ilastik and Flynotyper are used to quantify eye phenotype in an unbiased way.
08:02
Rendering SiO2/Si Surfaces Omniphobic by Carving Gas-Entrapping Microtextures Comprising Reentrant and Doubly Reentrant Cavities or Pillars
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Cited by 17 •
2020
This work presents microfabrication protocols for achieving cavities and pillars with reentrant and doubly reentrant profiles on SiO2/Si wafers using photolithography and dry etching. Resulting microtextured surfaces demonstrate remarkable liquid repellence, characterized by robust long-term entrapment of air under wetting liquids, despite the intrinsic wettability of silica.
02:54
Assessing the Role of Host Immunity in Mycobacterial Infection Using a Zebrafish Model
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2026
Source: Luukinen, H., et al. Modeling Tuberculosis in Mycobacterium marinum Infected Adult Zebrafish. J. Vis. Exp. (2018).
05:16
Single Drosophila Ommatidium Dissection and Imaging
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Cited by 1 •
2011
The limiting factor in the use of the adult Drosophila eye to study neurodegeneration and cell biology is the difficult imaging of intracellular processes. We describe the dissection of single ommatidia to generate a bona-fide primary neuronal cell culture, which can be subject to drug treatment and advanced imaging.
07:23
Electroporation of Craniofacial Mesenchyme
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2011
Craniofacial cartilages develop in close contact with other tissues and are difficult to manipulate in live animals. We are using electroporation to deliver molecular tools during growth of the craniofacial skeleton while bypassing early embryonic effects. This approach will allow us to efficiently test candidate molecules in vivo.
FAQs
The workflow combines user annotations with image-derived boundaries and connectivity. Foreground and background markings provide representative evidence about which regions belong to an object, while boundaries and connectivity help shape candidate masks in three dimensions. This combination lets users refine segmentation decisions without relying on extensive programming, especially when object geometry or contrast is difficult.
Foreground and background labels serve different roles: foreground indicates regions to retain as part of a target, whereas background identifies regions to exclude. Because users mark representative areas rather than every voxel, the software can use those examples alongside image information to propose masks. Careful labels therefore influence how effectively candidate objects reflect the intended structure.
Rapid visual feedback allows users to inspect candidate masks while annotating and immediately identify regions that require correction. This is particularly useful for volumetric images containing irregular shapes or variable contrast, where a single initial interpretation may not follow the desired structure. Iterative correction can produce more consistent segmentations for later analysis of neural architecture.
A typical workflow begins with a volumetric image and representative markings for foreground and background regions. The software then combines those annotations with image-derived boundaries and connectivity to generate a candidate object mask. Users review the result visually, add or adjust annotations where needed, and repeat the refinement cycle until the segmentation better matches the intended structure.
Neuroscientists can use the workflow when microscopy volumes contain structures that must be separated or traced in three dimensions. Supported use cases include tracing neurons, isolating cellular structures, and segmenting objects with irregular shapes or variable contrast. The interactive approach is useful when researchers need to guide segmentation directly rather than depend on extensive programming for complex image data.
Refined object masks can support quantitative analysis and reconstruction of structures in volumetric neuroscience data. They also provide a basis for downstream studies of neural architecture, because segmented neurons or cellular structures can be examined as distinct objects rather than as mixed image signal. Visual correction during annotation helps researchers produce results that are more consistent for these subsequent uses.