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Graylight Imaging
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Graylight Imaging - Measurements In Medical Imaging

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We’ve created and continue to develop algorithms for extracting valuable data from medical images various modalities. Take advantage of our know-how and capabilities to create customized solutions that will give you the quantitative data you need.

Most popular related searches
medical imaging
medical data
brain data

Our solutions based on machine learning automatise the segmentation process. The models we worked on contour organs and detect as well as localize structures within organs on various types of examinations.

Segmentation based on our deep learning method:

  • is precise and fast, provides repeatable results as well as enables extraction of measurable features of anatomical structures and lesions
  • has achieved an accuracy of more than 90% (Sørensen-Dice coefficient of roughly 0.9) [1] 
  • is characterized by effectiveness proven in many prestigious global AI challenges (such as BraTS and FeTS)
  • can be embedded into existing solutions
 

The bespoke algorithms we’ve been developing for our clients are designed to meet particular needs and requirements. They are able to measure and analyze as well as provide valuable quantitative data. This technology can be used to detect any lesion, for any internal anatomical structure of a single patient.

  • Detection of a single lesion on a scan, as well as multiple parts of a larger lesion
  • Evaluation of measurable lesion according to a predefined requirements
  • Lesions detection in time – detection of new lesions on a follow up scan 
  • Evaluation of patient response to treatment also outside standard criteria
Automated RANO and RECIST+

We created and applied fully automated algorithms for precise and comprehensive patient response measurements using RANO and RECIST criteria (automated RANO and automated RECIST) to real-world data. Furthermore, our technology is capable of much more than what the guidelines suggest.

Case study of automated RANO calculation

  • Volumetric measurements of predefined regions or subregions
  • Repeatability and objectivity of results
  • Integration with the segmentation algorithm, as all takes place in one step
  • Higher bidimensional measurements than those reported by most human readers [2]
 

Tumor parts segmentation and RANO calculation

Modality: MRI

Body part: brain

 

Input data

Sequences: T1, T1CE, T2, T2-FLAIR

 

Output data

Segmented necrosis: 14.907 cm3

Segmented edema: 99.434 cm3

Segmented enhancing tumor: 27.789 cm3

Segmented TOTAL: 142.13 cm3

Calculated RANO value: 1241.68 mm2