3D light-sheet microscopy data for SELMA3D 2026 challenge - contiguous structures - training patches without annotations

ABSTRACT: This dataset is the training set without annotations of contiguous structures for SELMA3D 2026 challenge. The SELMA3D 2026 challenge focuses on self-supervised learning for 3D light-sheet microscopy (LSM) image segmentation. Its objective is to encourage the development of generalizable models capable of serving multiple 3D LSM image segmentation tasks. This dataset contains cropped 3D patches from large images of different contiguous structures including blood vessels, arteries, peripheral nerves, cranial nerves, lymphatic vessels, sympathetic nerves and axons.

SUBMITTER: Ying Chen 

PROVIDER: S-BIAD3277 | bioimages |

REPOSITORIES: bioimages