Toward Generalizable Whole Brain Representations with High-Resolution Light-Sheet Data
Minyoung E. Kim, Dae Hee Yun, Aditi V. Patel, Madeline Hon, Webster Guan, Taegeon Lee, Brian Nguyen

TL;DR
This paper introduces CANVAS, a large high-resolution light-sheet microscopy dataset of the mouse brain, highlighting challenges in model generalization due to cellular heterogeneity and providing a benchmark for future research.
Contribution
The paper presents the first large-scale LSFM benchmark dataset with extensive annotations, addressing the need for scalable analysis methods for petabyte-scale brain imaging data.
Findings
Baseline models struggle to generalize across cellular heterogeneity.
CANVAS dataset captures detailed cellular morphology across the entire brain.
Heterogeneity in cell types and locations impacts model performance.
Abstract
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…
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