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#segmentation

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Kevin Moerman 🔓🦿

So how do you turn contours (see previous post) into surface geometry? Well you can create a "levelset image", it is a bit like a heat propagating positively outward and negatively inward from the contours. Instead of heat though you can use the distance to the contour. Next you can draw the isosurface at the level 0 to retrieve the surface.

If one segments stuff using voxel labels it can be hard to retrieve a smooth result. Here however the contours are smoothly interpolated curves and the isosurface is smoothly interpolated as well. Hence there is naturally less of a stepped-Lego issue :).

In the video below the image on the right is the wobbly STL that comes with the Visible Human project. I tried to do better based on the contours-level set approach. I can also control the surface mesh density as you can see in the middle image.

#opensource #Julialang #biomedicalengineering #finiteelementanalysis #biomechanics #segmentation

Open source projects used here:

github.com/COMODO-research/Ima

github.com/COMODO-research/Com

github.com/COMODO-research/Geo

1TE.CH ᳇ Tech News

#TargetGroups 🎯 Without effective #targetgrouporientation, #marketingmeasures are a pure game of chance: and without effective #dataanalysis, #marketing is blind.

#Segmentation 💬 Target groups are analyzed based on age, interests, behavior or location, for example, in order to develop tailored #marketingstrategies.

#Personalization 📊 #AI and #analysistools make it possible to provide and communicate personalized content to increase #conversions.

👉 OnlineMarketingStrategy.EU
👉 @marketing #news

eicker.BEratung · Online Marketing Strategy: Turn Your Target Groups Into Fans
More from Gerrit Eicker 🇪🇺🇺🇦
stgreenie

Check all the boxes...

Record your soul
into the master database
high in that cloud place
where thought #segmentation
will be statistically performed
to slot you properly
on the great cyber ship

Here you can dance on the platform
of the Microchip Ballroom
with other statistically like minded blips...

Take an opinion mining adventure...

Far above this contentious sphere...

Where filtered algorithm truth
will then be generously dispensed
by your benevolent
corporate government
in data cluster increments
for a quick conditioning trip
to our hermetically sealed thought silos

Peace through a uni-thought populace
is the ultimate destination

#vss365#poetry#poem
*
ALTA

In the lead up to #ALTA2024, we're highlighting #research papers from previous #workshops.

Here, the ChatGPT C-LARA-Instance, Belinda Chiera, Cathy Chua, Chadi Raheb, Manny Rayner, Annika Simonsen, Zhengkang Xiang, and Rina Zviel-Girshin use the #OpenSource #CLARA platform to evaluate #GPT4's ability to perform #linguistics #NLP tasks such as #segmentation, #lemmatization and #glossing.

🔗 C-LARA platform: c-lara.org/

🔗 Paper: aclanthology.org/2023.alta-1.3

C-LARAC-LARAA reimplementation of the LARA platform with ChatGPT in the centre
Haojia Wu

Cellpose V3 is out!
biorxiv.org/content/10.1101/20
#spatial #cellseg #pytorch #segmentation

bioRxiv · Cellpose3: one-click image restoration for improved cellular segmentationGeneralist methods for cellular segmentation have good out-of-the-box performance on a variety of image types. However, existing methods struggle for images that are degraded by noise, blurred or undersampled, all of which are common in microscopy. We focused the development of Cellpose3 on addressing these cases, and here we demonstrate substantial out-of-the-box gains in segmentation and image quality for noisy, blurry or undersampled images. Unlike previous approaches, which train models to restore pixel values, we trained Cellpose3 to output images that are well-segmented by a generalist segmentation model, while maintaining perceptual similarity to the target images. Furthermore, we trained the restoration models on a large, varied collection of datasets, thus ensuring good generalization to user images. We provide these tools as “one-click” buttons inside the graphical interface of Cellpose as well as in the Cellpose API. ### Competing Interest Statement The authors have declared no competing interest.
Haojia Wu

STCellbin utilizes the cell nuclei staining images as a bridge to align cell membrane staining images for obtaining accurate cell boundaries in Stereo-seq data #spatial #segmentation
biorxiv.org/content/10.1101/20

bioRxiv · Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary imagesStereo-seq is a cutting-edge technique for spatially resolved transcriptomics that combines subcellular resolution with centimeter-level field-of-view, serving as a technical foundation for analyzing large tissues at the single-cell level. Our previous work presents the first one-stop software that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With recent advancements in Stereo-seq technology, it is possible to acquire cell boundary information, such as cell membrane/wall staining images. To take advantage of this progress, we updated our software to a new version, named STCellbin, which utilizes the cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps. By employing an advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profiles. Experimental results verify the application of STCellbin on mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets. The improved capability of capturing single cell gene expression profiles by this update results in a deeper understanding of the contribution of single cell phenotypes to tissue biology. Availability & Implementation The source code of STCellbin is available at <https://github.com/STOmics/STCellbin>. ### Competing Interest Statement The authors have declared no competing interest. * DAPI : 4,6-diamidino-2-phenylindole H&E : hematoxylin-eosin ssDNA : single strand DNA fluorescence mIF : multiplex immunofluorescence CFW : calcofluor white FFT : Fast Fourier Transform.
Niklas Alt

Hi fediverse, is anyone aware of #opensource pipelines for #segmentation / #vectorization of #historical #cadastral maps? Ideally a workflow to train #AI / #ML models on specific mapsets, e.g. the new prussian survey after 1870*, the francisceian (mid 19th century) or the bavarian*** to only mention the largest surveys in central Europe. I suspect that people outside of history are working on it, these maps are a true treasure for #environmental and #biodiversity research. Links are in the reply

Oliver Mantell

A new role with the University of Leicester (in partnership with us at The Audience Agency) looking at how #AI can support cultural #segmentation: jobs.le.ac.uk/vacancies/8889/d

It is, of course, potentially a huge can of worms (!), but I'm hoping it will enable greater nuance, flexibility, learning and local/sector specificity - as well as more robust evidence of what works... (and done with partners who place social value and impact first).

University of Leicester Recruitment TeamData Scientist for Cultural Audience Segmentation (KTP Associate) in Other | University of Leicester Recruitment TeamView details and apply for this Data Scientist for Cultural Audience Segmentation (KTP Associate) vacancy in Other. This is an exciting opportunity for an ambitious Data Scientist with strong Machine Learning (ML) experience to create new methods to model and define customer segments for organisations in the cultu...
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katzenberger

In case it helps:

What data brokers call "#segmentation" is never meant to acknowledge what you self-identify as.

It's merely one harmful #label after the other slapped onto you. Harmful because its only purpose is to either extract more #profit from you, or to exclude you from services if you're likely to not contribute to somebody else's earnings.

There are no "mostly harmless" labels, even when those that serve to exclude you are the worst.

@pzriddle
@kentbrew

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