Enhancing MRI Analysis with
Prostate.Carcinoma.ai

Level Up Radiology with AI

With an incidence of 1.4 M worldwide, prostate cancer and its detection make up a significant amount of the work of a radiologist. Since MRI scans of the prostate prior to biopsy are highly recommended, capacities to handle the increasing workload need to be created.

Artificial intelligence (AI) has the potential to take prostate cancer diagnostics to a new level, providing faster, high-quality and time-efficient diagnoses.

AI and machine learning technologies facilitate the workflow by assisting in diagnosis, management and treatment of a wide variety of medical conditions, prostate cancer being one of them.

Based on this knowledge, the company FUSE-AI has developed a software that creates voxel-wise segmentations of the prostate, its zones and suspicious lesions.

  • Prostate MRI without AI

    Prostate MRI without AI

  • Automated segmentation of the prostate

    Automated segmentation of the prostate

  • Automated calculation of PSA density

    Automated calculation of PSA density

  • Automated segmentation of suspicious lesions

    Automated segmentation of suspicious lesions

  • Automated measuring of lesion size and mapping to lesion scheme

    Automated measuring of lesion size and mapping to lesion scheme

Characteristics of Prostate.Carcinoma.ai

Download these product information for using Prostate.Carcinoma.ai and its features.

Benefits of Prostate.Carcinoma.ai plug-in in mint Lesion

Simplify and speed up prostate screening according to PI-RADS 2.1

- Fully automated 3D segmentation of the prostate in MR T2 weighted images
- Detection and segmentation of prostate lesions in MR T2 weighted images
- Automatic mapping of lesions to the prostate lesion scheme

Light weighted integration via DICOM communication

- Image data is transferred back via DICOM segmentation objects

Extension possibilities

- New use cases can be integrated seamlessly without extending the communication interface

Are you interested in redefining
medical image analysis with us?

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