Bright 4B: Scaling Hyperspherical Learning for Segmentation in 3D Brightfield Microscopy
This work addresses a gap in 3D cell mapping by enabling label-free segmentation of organelles like nuclei and mitochondria, which is incremental as it builds on existing segmentation methods but applies them to a challenging microscopy modality.
The paper tackles the problem of robust volumetric segmentation in label-free 3D brightfield microscopy, which typically relies on fluorescence or heavy post-processing, by introducing Bright-4B, a 4 billion parameter foundation model that directly segments subcellular structures from brightfield volumes, outperforming CNN and Transformer baselines across multiple confocal datasets.
Label-free 3D brightfield microscopy offers a fast and noninvasive way to visualize cellular morphology, yet robust volumetric segmentation still typically depends on fluorescence or heavy post-processing. We address this gap by introducing Bright-4B, a 4 billion parameter foundation model that learns on the unit hypersphere to segment subcellular structures directly from 3D brightfield volumes. Bright-4B combines a hardware-aligned Native Sparse Attention mechanism (capturing local, coarse, and selected global context), depth-width residual HyperConnections that stabilize representation flow, and a soft Mixture-of-Experts for adaptive capacity. A plug-and-play anisotropic patch embed further respects confocal point-spread and axial thinning, enabling geometry-faithful 3D tokenization. The resulting model produces morphology-accurate segmentations of nuclei, mitochondria, and other organelles from brightfield stacks alone--without fluorescence, auxiliary channels, or handcrafted post-processing. Across multiple confocal datasets, Bright-4B preserves fine structural detail across depth and cell types, outperforming contemporary CNN and Transformer baselines. All code, pretrained weights, and models for downstream finetuning will be released to advance large-scale, label-free 3D cell mapping.