用H&E图像实现全细胞分割，突破常规染色限制
VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
- 通过跨模态学习，从mIF数据迁移细胞边界信息到H&E图像
- 在34种癌症、超700万实例上训练，性能超越现有方法
- 开源平台支持大规模推理，适合病理与空间组学研究者
准确的全细胞和核分割对精准病理学与空间组学至关重要，但常规苏木精-伊红（H&E）染色对比度低，仅能分析细胞核。多重免疫荧光（mIF）虽可精确分割全细胞，但受限于成本与可及性。本文提出VitaminP，一种跨模态学习框架，利用配对的H&E-mIF数据，将mIF中的分子边界信息迁移到H&E图像中，以恢复细胞质结构。我们在14个公开数据集（覆盖34种癌症类型）和超过700万实例上训练VitaminP，整合了公共标注与本研究生成的大量注释，构建了目前最大的分割资源之一。VitaminP在四项先进方法中表现更优，并成功泛化至未见数据集，包括涵盖24种罕见癌症的内部数据集。我们还开发了VitaminPScope，一个开源平台，提供可扩展的推理接口，推动广泛应用。
原文摘要 · Abstract (English)
Accurate whole-cell and nuclear segmentation is essential for precision pathology and spatial omics, yet routine hematoxylin and eosin (H&E) staining provides limited cytoplasmic contrast, restricting analyses to nuclei. Multiplex immunofluorescence (mIF) facilitates precise whole-cell delineation but remains constrained by cost and accessibility. We introduce VitaminP, a cross-modal learning framework enabling whole cell segmentation from H&E images. By learning from paired H&E-mIF data, VitaminP transfers molecular boundary information from mIF to overcome cytoplasmic contrast in H&E, establishing cross-modal supervision as a general strategy for recovering missing biological structure. We train VitaminP on 14 public datasets covering 34 cancer types and over 7 million instances, integrating publicly available labels with extensive annotations generated in this study, forming one of the largest resources for segmentation. VitaminP outperforms four state-of-the-art methods and generalizes to unseen datasets, including an in-house dataset spanning 24 rare cancer types. We further developed VitaminPScope, an open-source platform providing an interface for scalable inference and enabling broad adoption.
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