构建首个高分辨率小鼠全脑光片显微数据集，助力神经细胞分析
Toward Generalizable Whole Brain Representations with High-Resolution Light-Sheet Data
- 构建包含6种细胞标记物的高分辨率全脑数据集，支持亚细胞级分析
- 发现现有模型在不同脑区和细胞类型间泛化能力差，受形态差异影响大
- 适合神经科学、生物图像分析与医学影像领域研究人员使用
亚细胞分辨率的全脑三维显微数据正揭示前所未有的生物结构细节，得益于完整组织处理和光片荧光显微技术（LSFM）的进步。这些体数据提供丰富的细胞形态与空间信息，但海量（拍字节级）数据缺乏可扩展的处理与分析方法，阻碍了准确解读。现有视觉任务模型如目标检测与分类难以泛化到此类数据。为加速合适方法与基础模型的发展，我们提出CANVAS，一个包含六种神经与免疫细胞标记物、细胞注释及排行榜的高分辨率小鼠全脑LSFM基准数据集。我们还展示了基于现有架构的基线模型在泛化上的挑战，尤其因脑区和表型间的细胞形态异质性导致。据我们所知，CANVAS是首个且规模最大的在亚细胞水平捕获完整小鼠脑组织并包含全脑细胞详尽注释的LSFM基准数据集。
原文摘要 · Abstract (English)
Unprecedented visual details of biological structures are being revealed by subcellular-resolution whole-brain 3D microscopy data, enabled by recent advances in intact tissue processing and light-sheet fluorescence microscopy (LSFM). These volumetric data offer rich morphological and spatial cellular information, however, the lack of scalable data processing and analysis methods tailored to these petabyte-scale data poses a substantial challenge for accurate interpretation. Further, existing models for visual tasks such as object detection and classification struggle to generalize to this type of data. To accelerate the development of suitable methods and foundational models, we present CANVAS, a comprehensive set of high-resolution whole mouse brain LSFM benchmark data, encompassing six neuronal and immune cell-type markers, along with cell annotations and a leaderboard. We also demonstrate challenges in generalization of baseline models built on existing architectures, especially due to the heterogeneity in cellular morphology across phenotypes and anatomical locations in the brain. To the best of our knowledge, CANVAS is the first and largest LSFM benchmark that captures intact mouse brain tissue at subcellular level, and includes extensive annotations of cells throughout the brain.
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