Work

Light Sheet Microscopy Foundation Model Pretraining Dataset

Year: 2026

Type: dataset

Abstract: This dataset consists of 3D image patches extracted from light sheet microscopy (LSM) volumes with paired text descriptions. Images are drawn from five sources: an internal dataset from the Wu Lab at Weill Cornell Medicine (New York, NY, USA), and four public datasets: SELMA3D MICCAI challenge, Allen human brain, Allen developing mouse brain, and Allen viral labeling of projection neurons. From each dataset, up to 10 non-overlapping 96×96×96 voxel patches were randomly selected with a minimum foreground threshold of 5%, yielding 1,023 patches in total. The patches capture diverse neuroanatomical structures and imaging conditions across species and brain regions. Text descriptions were authored by a domain expert and paraphrased using an LLM to increase linguistic diversity while preserving semantic fidelity. This dataset was used for pretraining a multimodal LSM foundation model.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20149069

Authors: Scheinfeld, Adina, Zhang, Haotan, Mu, Shang, Ertürk, Ali, Wu, Zhuhao, Paetzold, Johannes C.

Institutions: Weill Cornell Medicine, Ludwig-Maximilians-Universität München

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7160993091

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