40亿参数模型直接从亮场显微图像分割细胞结构，无需荧光标记。
Bright 4B: Scaling Hyperspherical Learning for Segmentation in 3D Brightfield Microscopy
- 在单位超球面上学习，结合稀疏注意力与动态专家网络提升3D建模能力。
- 在多个共聚焦数据集上实现核、线粒体等细胞器的高精度分割，细节保留好。
- 适合做无标记3D细胞成像分析的研究者，代码模型将开源。
无标记3D亮场显微镜提供快速非侵入式细胞形态可视化，但鲁棒的体积分割仍依赖荧光或复杂后处理。本文提出Bright-4B，一个40亿参数的基础模型，通过在单位超球面上学习，直接从3D亮场体积中分割亚细胞结构。该模型融合硬件对齐的原生稀疏注意力（捕获局部、粗粒度及选定全局上下文）、深度-宽度残差超连接以稳定表征传播，以及软混合专家机制实现自适应容量。可插拔的各向异性块嵌入尊重共聚焦点扩散函数和轴向变薄特性，实现符合几何真实的3D令牌化。所提模型仅凭亮场堆栈即可生成核、线粒体等细胞器的形态准确分割结果，无需荧光、辅助通道或手工后处理。在多个共聚焦数据集上，Bright-4B在深度和细胞类型间均保持精细结构细节，优于现有CNN与Transformer基线。所有代码、预训练权重及下游微调模型将公开发布，推动大规模无标记3D细胞图谱构建。
原文摘要 · Abstract (English)
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.
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