Weak-to-Strong Generalization for Automated Cell Nuclei Segmentation in Brain Images

A methodology and supporting materials for automated training of a multi-head Mask-RCNN model to segment densely overlapping cell nuclei in multiplex whole-slide brain images. The dataset includes code, sample whole-slide images, and high-resolution segmentation results, published by Lin Bai in May 2026. The method was benchmarked against five other methods and showed significant improvement.

Use Cases

• Training domain-adapted segmentation models for multiplex spatial proteomics based on the described weak-to-strong generalization methodology.
• Benchmarking new cell nuclei segmentation algorithms against the provided high-resolution results.
• Developing automated self-diagnosis metrics for segmentation quality in production environments, as mentioned in the description.

Strengths

• The method was benchmarked against five current widely used methods and showed significant improvement.
• Includes code, sample whole-slide images, and high-resolution segmentation results for community adoption.
• Provides metrics for automated self-diagnosis of segmentation quality, addressing a key production challenge.

Limitations

• The dataset is small (563.1 KB), suggesting limited scope, likely containing documentation and sample images rather than a full training corpus.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale training.

Provenance

Source

figshare

Collection Method

Developed as part of a research methodology for weak-to-strong generalization in medical image segmentation.

Freshness

Last updated 2026-05-10 22:02:56