awesome-repositories.com
ब्लॉग
MCP
awesome-repositories.com

AI-संचालित खोज के साथ बेहतरीन ओपन-सोर्स रिपॉजिटरी खोजें।

एक्सप्लोर करेंक्यूरेटेड खोजेंओपन-सोर्स विकल्पसेल्फ-होस्टेड सॉफ्टवेयरब्लॉगसाइटमैप
प्रोजेक्टMCP सर्वरहमारे बारे मेंहम रैंकिंग कैसे करते हैंप्रेस
कानूनीगोपनीयताशर्तें
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 रिपॉजिटरी

Awesome GitHub RepositoriesSegment Quantitative Analysis

Calculating statistical and quantitative measurements from image segmentation masks.

Distinct from Image Segmentation: Focuses on extracting metrics from a segmentation, rather than the process of partitioning the image.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Segment Quantitative Analysis. Refine with filters or upvote what's useful.

Awesome Segment Quantitative Analysis GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • ohif/viewersOHIF का अवतार

    OHIF/Viewers

    4,035GitHub पर देखें↗

    Viewers is a zero-footprint DICOMweb medical imaging viewer and a modular plugin framework. It serves as a diagnostic interface for rendering 2D and 3D medical images, providing a web-based clinical workflow engine to automate image layouts and toolsets. The project distinguishes itself through a highly extensible architecture that allows for the development of custom clinical workflows, specialized viewing modes, and the integration of external functional extensions. It includes a dedicated command line interface for managing these plugins and supports white-labeling through a comprehensive

    Calculates and displays quantitative measurements for image segments to facilitate data analysis.

    TypeScriptcancer-imaging-researchdicomdicom-viewer
    GitHub पर देखें↗4,035
  • wasserth/totalsegmentatorwasserth का अवतार

    wasserth/TotalSegmentator

    2,482GitHub पर देखें↗

    TotalSegmentator is a medical image segmentation tool and AI-driven organ segmenter designed to isolate anatomical structures from CT scans. It functions as a deep learning anatomy parser and quantitative radiomics analyzer, providing a framework for identifying diverse body tissues and bones to create precise anatomical masks. The system distinguishes itself through a comprehensive medical analysis suite that includes patient biometric estimation for demographics such as age, sex, weight, and height. It further provides specialized clinical index calculations and modality and phase classific

    Computes volume and mean intensity from segmented masks to derive quantitative anatomical measurements and clinical indices.

    Python
    GitHub पर देखें↗2,482
  1. Home
  2. Artificial Intelligence & ML
  3. Computer Vision Systems
  4. Image Segmentation
  5. Segment Quantitative Analysis

सब-टैग एक्सप्लोर करें

  • Probability Map ExtractionsExtraction of raw model confidence scores and softmax probabilities for detailed uncertainty analysis. **Distinct from Segment Quantitative Analysis:** Distinct from Segment Quantitative Analysis: focuses on extracting confidence probabilities rather than derived anatomical metrics like volume.