SegCLR

Segmentation-Guided Contrastive Learning of Representations (SegCLR)

Segmentation-guided Contrastive Learning of Representations (SegCLR) is a method for learning rich embedding representations of cellular morphology and ultrastructure. See the blog post and the updated preprint fully describing the method for more details. The embeddings for two large-scale cortical datasets, one from human temporal cortex (explore in Neuroglancer) and one from mouse visual cortex (explore), are publicly released on Google Cloud Storage.

SegCLR code release and demo notebooks

Open-source release of SegCLR code to the connectomics repo is in progress. Colab notebooks demonstrating how to use the code are available here:

• Access SegCLR embeddings from public CSV ZIP releases for h01 (human cortex) and MICrONS (mouse cortex). This notebook shows how to read the data remotely and parse it. It also demonstrates how to run dimensionality reduction to inspect embedding clusters (as in paper figure 4).
• Run a pretrained SegCLR subcompartment classifier. This notebook shows how to load a pretrained subcompartment classifier model and run it on embeddings for a test cell (as in paper figure 2).

Further demo notebooks will be provided as the code is released.