Shout out to the whole team: @drannecarpenter.bsky.social, @shantanu-singh.cc , @maom.bsky.social! 🙌

📖 Paper: openaccess.thecvf.com/content/ICCV...
💻 Code: github.com/alxndrkalini...
6/6

We show how cubic can accelerate existing workflows by 10–1500×, including a 3D cell segmentation tutorial from CellProfiler. ⚡
5/6

cubic keeps things familiar: swap imports, put your image on the device you want, and the same function names automatically dispatch to the right CPU/GPU implementation—optional acceleration with minimal refactoring. ✨
4/6

CuPy-based cuCIM mirrors much of scikit-image, but uses device-specific function signatures that must match the input array’s device—typically leading to substantial refactoring to add GPU support to existing codebases.
3/6

scikit-image is widely used across bioimage analysis (incl. under the hood in CellProfiler), but with large 3D volumes and long time-lapse datasets, CPU execution often becomes the bottleneck.
2/6

In applications, there are going to be some fun CV presentations in the GenBio workshop - come check it out!

Disclaimer: I have one of those and would love deeper critique from a CV standpoint:
bsky.app/profile/alxn...

Workshop webpage: genbio-workshop.github.io/2025/
#ICML2025 #ICML #GenAI #GenBio #BioML #BioimageAnalysis

📄 Dive into the details in our preprint → arxiv.org/abs/2507.05383

I’ll be presenting this work at the GenBio workshop at ICML on Friday, July 18 — come say hi and chat about virtual staining!

Big cheers to our collaborators at @imbavienna.bsky.social & @umich.edu Medical School

5/5

The visual difference is clear: compared to a baseline (F-net), Spotlight sharply reduces artifacts, resulting in clearer nuclear boundaries and less segmentation artifacts, while preserving foreground textures.

4/5

💡Spotlight uses the fact that even simple histogram thresholding (e.g., Otsu) is often sufficient to approximate informative FG regions. We use this to (1) mask MSE loss to focus learning on FG intensities, and (2) add a FG/BG segmentation loss to preserve cell morphology.

3/5

Most VS models are trained with pixel-wise losses like MSE, treating background (BG) and foreground (FG) equally. Unlike natural images, BG in cell imaging isn't informative–so models learn to reproduce noise. E.g., in 3D, predictions show axial blur and elongation.

2/5

Looks neat! How does the number/variety of features compare to Cellprofiler? Does it have Python bindings?

Big shout out to the whole team: Alán F. Muñoz, Tim Treis, @shatavishadg.bsky.social , @fabiantheis.bsky.social ntheis.bsky.social, @drannecarpenter.bsky.social, @shantanu-singh.cc

6/6

We will be discussing this work at #ICML2025 CODEML workshop - come check it out and chat with us!

📖 Arxiv → arxiv.org/abs/2507.01163
💻 Github → github.com/afermg/cp_me...

5/6

Key benefits:
- Reproducibility: replaces GUI workflows with code
- General: agnostic to data types (3D images, spatial transcriptomics)
- Few dependencies: easy to integrate into existing image analysis frameworks
- Backwards-support: largely matches CellProfiler features

4/6

With that in mind, we developed cp_measure, a Python library that extracts morphological features from segmented images from within your pipeline, bridging the gap between the BioAI/ML community and the existing GUI-based tool that populates bioimaging workflows.

3/6

We felt there were a limited number of programmatic tools for featurizing segmented cell images, and CellProfiler is the de-facto standard for interpretable features.

2/6