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VBrain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors and organs at risk in the brain (i.e., the region of interest, ROI) on axial T1 contrast-enhanced brain MRI images. VBrain is intended to be used on adult patients only. VBrain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor and organs at risk (brain stem, eyes, optic nerves, optic chiasm) in the brain on MRI images for trained medical professionals' attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain does not alter the original MRI image, nor does it intend to detect tumors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases, meningiomas, and acoustic neuromas, and contours of organs at risk in the brain; it is not intended to be used with images of other brain tumors or other body parts. The user must know the tumor type when they use VBrain. VBrain also contains the automatic image registration feature to register volumetric medical image data. (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images. Medical professionals must finalize (confirm or modify) the contours generated by VBrain, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.

VBrain is a software application system intended for use in the contouring (segmentation) of brain MRI images and in the registration of multi-modality images. The device consists of a workflow management module and 3 algorithm modules, which are the tumor contouring algorithm module, OAR contouring algorithm module, and registration algorithm modules can work independently, and yet can be integrated with each other. The tumor contouring (segmentation) algorithm module consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to contour brain tumor on the axial T1 contrast-enhanced MR images. It utilizes deep learning neural networks to generate contours for the detected/diagnosed brain tumors and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT). The OAR contouring (segmentation) algorithm module consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to contour organs at risk in the brain on the axial T1 contrast-enhanced MR images. It utilizes deep learning neural networks to generate contours for the organs at risk in the brain and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT). The registration algorithm module registers volumetric medical image data (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images. The workflow management module is configured to work on a PACS network. Upon user's request, it will pull patient scans from a PACS, and it will trigger a predefined workflow, in which different algorithm modules are executed to generate the DICOM output. The DICOM output of a workflow can be sent back to the PACS.

VBrain-OAR is a software device intended to assist trained radiotherapy personnel including, but not limited to, radiologists, radiation oncologists, neurosurgeons, radiation therapists, and medical physicists, during their clinical workflows of brain tumor radiation therapy treatment planning, by providing initial object contours of organs at risk in the brain (i.e., the region of interest, ROI) on axial T1 contrast-enhanced brain MRI images. VBrain-OAR is intended to be used on adult patients only. VBrain-OAR uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) organs at risk (brain stem, eyes, optic nerves, optic chiasm) in the brain on MRI images for trained radiotherapy personnel's attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain-OAR does not alter the original MRI image, nor does it intend to detect tumors for diagnosis. VBrain-OAR is intended only for contouring and generating contours of organs at risk in the brain; it is not intended to be used with images of other body parts. VBrain-OAR also contains the automatic image register volumetric medical image data. (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images. Radiation therapy treatment personnel must finalize (confirm or modify) the contours generated by VBrain-OAR, as necessary, using an external platform available at the facility that supports DICOM-RT viewinglediting functions, such as image visualization software and treatment planning system.

VBrain-OAR is a software application system indicated for use in the contouring (segmentation) of brain MRI images for the organs at risk (OAR) in the brain during radiation treatment planning and in the registration of multi-modality images. The device consists of 2 algorithm modules, which are contouring algorithm module and registration algorithm module, and a workflow management module. The modules can work independently, and yet can be integrated with each other. The contouring (segmentation) algorithm module consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to contour organs at risk in the brain on the axial T1 contrast-enhanced MR images. It utilizes deep learning neural networks to generate contours for the organs at risk in the brain and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT). The registration algorithm module registers volumetric medical image data (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images. The workflow management module is configured to work on a PACS network. Upon user's request, it will pull patient scans or users can send corresponding DICOM images, and it will trigger a predefined workflow, in which different algorithm modules are executed to generate the DICOM output. The DICOM output of a workflow are sent back to the PACS.

VBrain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors (i.e., region of interest, ROI) on axial T1 contrast-enhanced brain MRI images. VBrain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor on MRI images for trained medical professionals' attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain does not alter the original MRI image, nor does it intend to be used to detect tumors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases, meningiomas, and acoustic neuromas on axial T1 contrast-enhanced MRI images; It is not intended to be used with images of other brain tumors. The user must know the tumor type when they use VBrain. VBrain is intended to be used on adult patients only. Medical professionals must finalize (confirm or modify) the contours generated by VBrain, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.

VBrain is a software device indicated for use in the analysis of brain MRI images. The device consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to assist trained medical professionals, during clinical workflows of radiation therapy treatment planning, by highlighting and contouring known (diagnosed) brain tumors on the axial T1 contrast-enhanced MRI images. The software is configured to work on a PACS network. Upon user's request, it will patient scans or users can send corresponding MR images, and the device will utilize deep learning neural networks to generate contours for the detected/diagnosed brain tumors and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT) back to the network. The medical professionals must finalize (confirm and modify) the contours produced by VBrain as necessary using an external platform that supports RT DICOM viewing/editing, such as a treatment planning system.