Systematic Review of Pituitary Gland and Adenoma MRI Segmentation Techniques

34 studies on automatic and semi-automatic segmentation methods for pituitary glands and adenomas in MRI were analyzed from a pool of 353 reviewed studies. The review, authored by Mubaraq Yakubu and last updated in April 2026, synthesizes data on techniques and performance metrics like Dice scores. It is published as a PDF under a CC-BY-4.0 license on figshare.

Use Cases

• Benchmarking segmentation algorithm performance based on reported Dice scores for gland and adenoma segmentation.
• Surveying the prevalence of deep learning approaches like U-Net models in this medical imaging domain.
• Identifying research gaps for future methodological innovation based on unreported metrics like MR field strength or adenoma size.
• Informing the creation of larger, more diverse training datasets to enhance clinical applicability.

Strengths

• Provides a quantitative synthesis of performance metrics, reporting Dice score ranges for automatic (0%-96%) and semi-automatic (75%-92%) methods.
• Analyzes a defined corpus of 34 relevant studies from a systematic review of 353 studies.
• Explicitly registered with PROSPERO (CRD42023407127), indicating a structured review methodology.

Limitations

• The dataset is a 169.3 KB PDF document, a text-based review, not a primary image or tabular dataset for direct model training.
• The review notes that most studies did not report important contextual metrics like MR field strength, patient age, or adenoma size.
• Row count and column-level documentation are absent; the data consists of synthesized findings from reviewed literature.

Provenance

Source

figshare, author Mubaraq Yakubu.

Collection Method

Systematic review and data synthesis from 34 selected medical imaging studies.

Time Range

Covers studies included in the systematic review; specific publication years of reviewed studies are not provided.

Freshness

Last updated 2026-04-10 05:57:48.

Geography

Not specified; likely global given the nature of a systematic review.