Summary
- Profile Type
- Technology offer
- POD Reference
- TOES20240705003
- Term of Validity
- 5 July 2024 - 5 July 2025
- Company's Country
- Spain
- Type of partnership
- Research and development cooperation agreement
- Commercial agreement with technical assistance
- Targeted Countries
- All countries
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General information
- Short Summary
- A Spanish SME has developed a platform for automatic medical imaging segmentation that allows to upload images and segmentations of any modality and subsequently generate an automated AI model based on the image specific characteristics. It's able to segment large amounts of medical images and quickly develop imaging biomarkers. They are looking for entities interested in signing commercial agreement with technical assistance or participating in research procects.
- Full Description
-
Medical imaging segmentation is a time-consuming task. For many clinical and validation studies it is necessary to label the images by manual means. Automatic segmentation tools exist in the market, but they are all focused on specific images and issues, additionally they usually require some type of pre-processing steps or are limited to some settings such as specific image modalities, image resolutions or image orientations.
On the other hand, developing a customized automated segmentation software requires several expensive resources such as AI expertise, expensive hardware and large amounts of labelled images.
This innovative segmentation platform overcomes all these limitations. The user can start by labelling a minimum of 6 examples and with this the system will automatically generate a customized automatic segmentation AI model for their specific problem. The system does not require any type of image pre-processing and users can use the images obtained directly from the source machines. The system can work with both DICOM and NIFTI file formats, including their compressed variants.
The user is not limited to use a single image type for their segmentation problem, if they have several imaging modalities they can employ them all as long as they are correctly co-registered beforehand (for example using both T1 and T2 in an MRI setting, or both a PET and CT images). Additionally the system is orientation-agnostic. The user can mix images acquired in different planes (sagittal, coronal, axial or any other, for instance cardiac-planes), the only requirement is that every image-segmentation pair follows the same orientation. This also applies for predicting new segmentations, the user can provide images in any plane without any additional work.
The new segmented images can then be assessed and manually rated to automatically refine the segmentation model, making the entire process dynamic and iterative, this allows to increase the model capabilities by selecting the best results without additional manual labelling required.
The company is looking for partners working in the development of novel imaging biomarkers interested in signing a commercial agreement with technical assistance. They can benefit from the use of a cloud-based system, suitable for any image modality that is commercialized as a SaaS service with a periodic license fee. The company is also open to collaborate in the framework of an R&D project that requires the characterization of the images from large cohorts of patients. - Advantages and Innovations
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The overall main innovation is the ability to automatically generate a personalized, easy-to-use, medical imaging AI segmentation model. Compared to other automated segmentation solutions this one is modality-agnostic, resolution-agnostic, image size-agnostic and orientation-agnostic. It is adaptable to any image type and segmentation target. It needs an upload of only six labelled images to start the process.
There are many segmentation tools available but all focus on specific imaging and target problems but none with the focus on fully automated segmentation for new problems. This is an understandable scenario since developing a single automatic AI segmentation model requires great number of resources. Due to the fast medical radiology development and new targets of study and characterization requirements, it is imperative to produce new segmentation models. This innovative platform is able to do it quickly and cheaply. - Stage of Development
- Already on the market
- Sustainable Development Goals
- Goal 3: Good Health and Well-being
Partner Sought
- Expected Role of a Partner
- The company is looking for partners working in the development of novel imaging biomarkers interested in signing a commercial agreement with technical assistance. They can benefit from the use of a cloud-based system, suitable for any image modality that is commercialized as a SaaS service with a periodic license fee. The company is also open to collaborate in the framework of an R&D project that requires the characterization of the images from large cohorts of patients.
- Type and Size of Partner
- SME 11-49
- SME <=10
- Big company
- R&D Institution
- SME 50 - 249
- University
- Other
- Type of partnership
- Research and development cooperation agreement
- Commercial agreement with technical assistance
Dissemination
- Technology keywords
- 06001005 - Diagnostics, Diagnosis
- 06001012 - Medical Research
- 06005004 - Remote diagnostics
- 01004001 - Applications for Health
- Market keywords
- 05002003 - Ultrasound imaging
- 05002005 - Other medical imaging
- 05002004 - Nuclear imaging
- 05002002 - CAT scanning
- 05002001 - X-rays
- Sector Groups Involved
- Health
- Targeted countries
- All countries
Files
Examples.pdf