Redbrick AI's Fast Automated Segmentation Tool (F.A.S.T) is designed to assist healthcare professionals in annotating and segmenting various types of medical images, such as CT scans, MRI images, and ultrasounds. It can divide images into parts and adapt to new image types without the need for additional data.
In the context of medical imaging, SAM has shown strong performance by effectively segmenting visible objects or features, including large organ segmentation in CT and MRI scans. The tool's interactive nature allows clinicians to witness the computation of the mask in real time as they prompt the model. This real-time interaction simplifies accurate segmentation. F.A.S.T. can automate a large portion of the manual segmentation process, making it easier for teams to classify automatically generated segmentations.
RedBrick AI provides a SaaS platform for annotating medical image data and offers F.A.S.T. for use in the radiology domain, presenting a possible solution for enhancing diagnostic accuracy and speed in healthcare.
Currently, the tool seems to be focused on radiology as opposed to hematology. A hypothetical application of F.A.S.T. could be in the field of hematology. For instance, a hematologist could use F.A.S.T. to analyze blood smear images, which are vital for diagnosing various blood disorders. The tool could segment the different blood components – red blood cells, white blood cells, and platelets – more quickly than manual methods. This potential application is plausible given F.A.S.T.'s capabilities, though it should be noted that this specific use in hematology is conjecture and has not been explicitly documented.
RedBrick AI is a purpose built application to assist medical imaging teams in annotating medical data like CT, MRI, Ultrasound, etc. Everything from data-upload, the annotation tools have been built with medical workflows in mind.
RedBrick AI is the home-base for your data efforts. The platform is built for annotators/clinicians, engineers and project managers. Easily involve your entire team in building high quality datasets.
