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ATLAS-1: 3D Drug-Treated Cancer Cells
ATLAS-1 is a research dataset of single-cell 3D morphologies captured under different drug treatments.
It contains 1,500 WM266-4 human melanoma cells embedded in collagen and imaged using oblique-plane light-sheet microscopy. Each cell is released as a 1024-point point cloud sampled from a reconstructed surface mesh.
- Total cells: 1,500
- Classes / conditions:
nocodazole(500) — microtubule inhibitor; rounded morphologyblebbistatin(500) — myosin II inhibitor; elongated/spindle morphologycontrol(500) — untreated baseline variability
- Representations: point clouds (1024 pts/cell) and watertight meshes
- Intended use: benchmarking point-cloud models on real biological shapes; studying drug-induced 3D morphology
This dataset accompanies the PointMIL project (inherently interpretable point-cloud classification via MIL).
Dataset Summary
Cells were segmented, meshed with marching cubes (+ Laplacian smoothing), then uniformly sampled to 1024 points per cell from the surface mesh. Files are organized by condition with a metadata CSV that holds labels and basic experiment info.
Why ATLAS-1?
- Real, noisy, heterogeneous 3D cell shapes (not synthetic CAD)
- Clear treatment effects (rounded vs elongated) for interpretable benchmarks
- Comes with both mesh and point-cloud views for flexible pipelines
Repository Layout
🚀 PointMIL is an ICCV 2025 Highlight — powering interpretable 3D cell shape analysis at Sentinal4D
At Sentinal4D, we’re pioneering the analysis of 3D cell shapes, and we believe models should be trustworthy by design.
PointMIL is our unified Multiple Instance Learning (MIL) framework that delivers state-of-the-art accuracy across 3D biomedical datasets and inherent interpretability.
Why this matters for biology
Mapping drug effects in 3D: See which cell regions the drug affects, e.g., rounding vs. elongation, lost protrusions, altered curvature.
New dataset, ATLAS-1: Drug-treated 3D melanoma cells in collagen (control, nocodazole, blebbistatin) to evaluate both performance and interpretability on real data. Available on Hugging Face 🤗
Higher accuracy than black-box models.
Plug-in head: Works with popular point-cloud backbones.
Try it for yourself:
🌐 Project page + live demo: https://sentinal4d.github.io/PointMIL/
💾 Code: https://github.com/Sentinal4D/PointMIL/
🧪 Dataset (ATLAS-1): https://sentinal4d.github.io/ATLAS-1/
If you work on cell morphology, drug discovery, or explainable 3D AI, we’d love your feedback and collaborations.
#ICCV2025 #Sentinal4D #PointMIL #3D #CellBiology #Morphology #ExplainableAI #XAI #ComputerVision #MIL #OpenScience