WE ARE GRAYLIGHT IMAGING – YOUR PARTNER IN
Measurements in medical imaging
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.
LEARN MORE ABOUT OUR SOLUTIONS FOR AUTOMATED MEDICAL IMAGE SEGMENTATION
Medical image segmentation with machine learning
Our solutions based on machine learning automatise the segmentation process. The models we worked contour organs and detect as well as localize structures within organs on various types of examinations.
Segmentation based on our deep learning method:
OUR CAPABILITIES TO CREATE CUSTOM SOLUTIONS
Extraction and measurement of quantitive data from medical images
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
Tumor parts segmentation and RANO calculation
Body part: brain
Sequences: T1, T1CE, T2, T2-FLAIR
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
Automated segmentation examples
 Filip Malawski, Jarosław Gośliński, Mikołaj Stryja, Katarzyna Jesionek, Marcin Kostur, Karol Miszalski-Jamka, Jakub Nalepa, Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA, Lecture Notes in Computer Science book series (LNCS,volume 13540), 2022
 Jakub Nalepa, Krzysztof Kotowski, Bartosz Machura, Szymon Adamski, Oskar Bozek, Bartosz Eksner, Bartosz Kokoszka, Tomasz Pekala, Mateusz Radom, Marek Strzelczak, Lukasz Zarudzki, Agata Krason, Filippo Arcadu, Jean Tessier, Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients, Computers in Biology and Medicine, Volume 154, 2023, https://doi.org/10.1016/j.compbiomed.2023.106603