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#ImageAnalysis

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Karsten Schmidt

To avoid a massive OpenCV dependency for a current project I'm involved in, I ended up porting my own homemade, naive optical flow code from 2008 and just released it as a new package. Originally this was written for a gestural UI system for Nokia retail stores (prior to the Microsoft takeover), the package readme contains another short video showing the flow field being utilized to rotate a 3D cube:

thi.ng/pixel-flow

I've also created a small new example project for testing with either webcam or videos:

demo.thi.ng/umbrella/optical-f

GIF
#ThingUmbrella#OpticalFlow#ImageAnalysis
Public
David Mason

I left the other place in one of the early big migrations, but I'm delighted that my employer has also followed suit:

bsky.app/profile/visiopharm.bs

If you're over there and want to see what the company is doing in the #DigitalPathology #DeepLearning #ImageAnalysis space, check it out ... or say hello and ask me :)

Bluesky SocialVisiopharm (@visiopharm.bsky.social)Visiopharm® is a world leader in AI-driven digital precision pathology software.
Public
Moritz Negwer

New deep-learning cell detection pipeline for light-sheet mouse brain image stacks, with an interesting cell-coordinate clustering statistics approach:

A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy
Attarpour et al., Nature Methods 2025
doi.org/10.1038/s41592-024-025

Code: github.com/AICONSlab/MIRACL

Documentation: miracl.readthedocs.io/

Fig. 1 a–c, Intact whole brains immunolabeled, cleared and imaged with LSFM were used as input to the ACE pipeline. a, Whole-brain LSFM data are passed to ACE’s segmentation module, consisting of ViT- and CNN-based DL models, to generate binary segmentation maps in addition to a voxel-wise uncertainty map for estimation of model confidence. b, The autofluorescence channel of data is passed to the registration module, consisting of MIRACL registration algorithms, to register to a template brain such as the Allen Mouse Brain Reference Atlas (ARA). High-resolution segmentation maps are then voxelized using a convolution filter and warped to the ARA (10 µm) using deformations obtained from registration. c, Voxelized and warped segmentation maps are passed to ACE’s statistics module. Group-wise heatmaps of neuronal density are obtained by subtracting the average of warped and voxelized segmentation maps in each group to identify neural activity hotspots. To identify significant localized group-wise differences in neuronal activity in an atlas-agnostic manner, a cluster-wise, threshold-free cluster enhancement permutation analysis (using group-wise ANOVA) is conducted. The resulting P value map represents clusters showing significant differences between groups. Correspondingly, ACE outputs a table summarizing these clusters, including their volumes and the portion of each brain region included in each cluster.
#lightsheet#microscopy#ImageAnalysis
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