[3.10-API] API for Retrieving Segmentation Metrics and Statistics

[sedghi]

Purpose
This feature introduces a new API for retrieving detailed metrics and statistics on medical image segmentations. The API supports both individual labelmap segmentations and groups of overlapping segmentations, allowing users to access a range of metrics such as intensity values, metabolic tumor volume, lesion glycolysis, and more. This API is designed to enable advanced data analysis for researchers and clinicians working with segmentation data.

Why This Matters
Segmentation metrics are critical for evaluating and analyzing medical images in clinical research and diagnostic applications. By providing a standardized way to retrieve key metrics, this API enables users to perform quantitative analysis on segmentation data, helping them to derive insights on tumor characteristics, disease progression, and treatment response. This capability is especially important in PET/CT imaging, where metrics like SUV peak and lesion glycolysis are used in oncological assessments.

Key Changes

• Detailed Metric Retrieval: Enables access to statistics such as mean, median, minimum, and maximum intensity values, standard deviation, SUV peak, and lesion glycolysis for individual segmentations.
• Support for Overlapping Segmentation Statistics: Provides aggregated statistics for sets of overlapping segmentations, allowing analysis across multiple regions of interest.
• Modality LUT Adjustment: Where applicable, intensity values are adjusted using the Modality LUT for accurate representation; otherwise, raw values are returned.

Impact on Users and Developers

• For Users: This API allows users to perform advanced segmentation analysis directly from the platform, simplifying workflows in research and clinical settings.
• For Developers: The API offers a consistent and efficient way to retrieve segmentation metrics, making it easier to integrate detailed quantitative analysis into custom applications or research tools.