Liu et al. MIBI-TOF Data: Lymph Node Tissue Images with Pixel and Cell Segmentation

MIBI-TOF data for a lymph node dataset includes single-channel 1024x1024 pixel TIFF images for multiple markers. The data includes segmentation output from the Mesmer tool and source CSV files containing pixel-level cluster consistency scores for various preprocessing parameters. The dataset was created by Candace C. Liu from Stanford University.

Use Cases

• Benchmarking pixel-level clustering algorithms based on the provided cluster consistency scores for different preprocessing steps.
• Training or validating cell segmentation models using the provided Mesmer segmentation masks on MIBI-TOF images.
• Analyzing the spatial composition of cell types in lymph node tissue based on multiplexed marker images and segmentation data.
• Studying the effect of image preprocessing parameters like Gaussian blur sigma on clustering outcomes using the provided comparison tables.

Strengths

• Includes high-resolution 1024x1024 pixel images for multiple biological markers.
• Provides cell segmentation masks generated by a published tool (Mesmer), enabling single-cell analysis.
• Contains extensive source data files comparing cluster consistency scores across numerous preprocessing variations.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and total dataset size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Stanford University (Candace C. Liu), associated with the paper 'Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering'.

Collection Method

Mass spectrometry imaging (MIBI-TOF) of lymph node tissue, with computational cell segmentation and pixel clustering.