面向可泛化全脑表征的高分辨率光片数据
Toward Generalizable Whole Brain Representations with High-Resolution Light-Sheet Data
提供了首个亚细胞分辨率全脑光片荧光显微镜基准数据集，可用于评估和开发面向大规模三维生物医学数据的检测与分类模型，并揭示了现有架构在细胞形态异质性下的泛化挑战。
原文依据 we present CANVAS, a comprehensive set of high-resolution whole mouse brain LSFM benchmark data
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论亚细胞分辨率的全脑三维显微数据正在揭示生物结构前所未有的视觉细节，这得益于完整组织处理与光片荧光显微镜（LSFM）的最新进展。这些体数据提供了丰富的形态与空间细胞信息，然而，缺乏针对这些PB级数据量身定制的可扩展数据处理与分析方法，对准确解读构成了重大挑战。此外，现有用于目标检测和分类等视觉任务的模型难以泛化到此类数据。为加速开发合适的方法与基础模型，我们提出了CANVAS，一套全面的高分辨率全小鼠脑LSFM基准数据，涵盖六种神经元和免疫细胞类型标记物，并包含细胞标注和排行榜。我们还展示了基于现有架构构建的基线模型在泛化性方面的挑战，特别是由于大脑中不同表型和解剖位置的细胞形态异质性。据我们所知，CANVAS是首个也是最大的LSFM基准，以亚细胞水平捕获完整小鼠脑组织，并包含全脑细胞的广泛标注。
英文原文摘要 1306 字符
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.