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.

Device Description

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.

AI/ML Overview

This document, a 510(k) premarket notification for Vysioneer Inc.'s VBrain-OAR, focuses on the device's technical characteristics and claims of substantial equivalence to predicate devices, but does not provide the specific acceptance criteria and detailed study results (including performance metrics like Dice Similarity Coefficient, Hausdorff Distance, or expert review scores) that would typically be required to fully describe how the device met these criteria.

The document states that "performance testing was conducted to evaluate the contouring (segmentation) performance and registration performance of VBrain-OAR" and that "the auto-segmentation algorithm of the VBrain-OAR algorithm module provides clinically acceptable contours for organs at risk in the brain structures on an image of a patient." However, it does not explicitly define what constitutes "clinically acceptable" or provide the quantitative results from these tests.

Therefore, many of the requested details cannot be extracted directly from the provided text. I will provide information based on what is available and indicate where details are missing.

Acceptance Criteria and Device Performance Study (Based on Provided Text)

The document generally states that the device's performance was evaluated, and it met "clinically acceptable" standards. However, the specific quantitative acceptance criteria (e.g., minimum Dice Similarity Coefficient, maximum Hausdorff Distance) are not detailed in the provided text. Similarly, the reported device performance (quantitative results) against these criteria is also not included in this summary.

In the absence of specific acceptance criteria and performance results, the table below is illustrative of what would typically be included in such a section, but the "Acceptance Criteria" and "Reported Device Performance" columns cannot be filled with concrete numbers from the provided document.

1. Table of Acceptance Criteria and Reported Device Performance

Feature/Metric	Acceptance Criteria	Reported Device Performance
Contouring (Segmentation) Performance	Not explicitly stated in document (e.g., Min. Dice Similarity Coefficient, Max. Hausdorff Distance, Expert Review Score)	Not explicitly stated in document (e.g., Achieved Dice scores, Hausdorff distances, Qualitative assessment)
Brain Stem Contouring	Clinically acceptable*	Clinically acceptable*
Eyes Contouring	Clinically acceptable*	Clinically acceptable*
Optic Nerves Contouring	Clinically acceptable*	Clinically acceptable*
Optic Chiasm Contouring	Clinically acceptable*	Clinically acceptable*
Registration Performance	Not explicitly stated in document (e.g., Registration accuracy in mm)	Not explicitly stated in document (e.g., Achieved registration accuracy)
Rigid image registration	Substantially equivalent to predicate device	Substantially equivalent to predicate device

Note: The term "clinically acceptable" is used in the document but is not defined with quantitative metrics.

2. Sample Size Used for the Test Set and Data Provenance

Sample Size for Test Set: The document states "VBrain-OAR was tested on datasets from multiple institutions" for standalone performance testing. However, the exact number of cases or scans in the test set is not specified.
Data Provenance: The document mentions "datasets from multiple institutions" and "data across patient sex, multiple imaging hardware and protocols" was used for testing. However, the country of origin of the data is not specified. The document also does not explicitly state whether the data was retrospective or prospective, though typical 510(k) submissions for AI devices often rely on retrospective datasets for performance testing.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The provided document does not specify the number of experts used to establish the ground truth for the test set, nor does it detail their qualifications (e.g., radiologist with X years of experience). It's implied that "trained radiotherapy personnel" are involved in the standard practice of manual contouring which the device aims to assist, but this does not directly describe the ground truth establishment process for the test data.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) used for establishing the ground truth for the test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The provided summary does not indicate that an MRMC comparative effectiveness study was performed to assess how human readers improve with AI vs. without AI assistance. The device is described as an "assistance" tool, but an MRMC study demonstrating improvement in human performance is not mentioned. The focus is on the standalone performance of the AI algorithm.

6. If a Standalone (Algorithm Only) Performance Study Was Done

Yes, a standalone performance study was done. The document explicitly states under "5.7 Non-Clinical Test (Standalone Performance Data)":
"Standalone performance testing was conducted to evaluate the contouring (segmentation) performance and registration performance of VBrain-OAR."

7. The Type of Ground Truth Used

The type of ground truth used is implied to be expert consensus or expert-derived manual contours. The device aims to "provide initial object contours... on axial T1 contrast-enhanced brain MRI images" and states it's "not intended for replacing their current standard practice of manual contouring process." This suggests that human expert manual contours would serve as the ground truth against which the AI's generated contours are compared. However, the exact methodology for establishing this ground truth (e.g., single expert, multi-expert consensus) for the test set is not detailed.

8. The Sample Size for the Training Set

The document does not specify the sample size used for the training set. It mentions the use of "deep learning neural networks" implying a training phase, but provides no details on the data used.

9. How the Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for the training set was established. While it is implied that expert manual contours would be used given the device's function, the details of this process for the training data are not provided.

Summary

{0}------------------------------------------------

October 12, 2021

Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the seal of the Department of Health & Human Services. To the right of the seal is the FDA logo, which consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, and the word "ADMINISTRATION" in a smaller font below.

Vysioneer Inc. % Vicki Lin Regulatory Specialist 33 Rogers St., # 308 CAMBRIDGE MA 02142

Re: K212116

Trade/Device Name: VBrain-OAR Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QKB Dated: September 10, 2021 Received: September 13, 2021

Dear Vicki Lin:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see

{1}------------------------------------------------

https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

{2}------------------------------------------------

Indications for Use

510(k) Number (if known) K212116

Device Name VBrain-OAR

Indications for Use (Describe)

V Brain-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.

V Brain-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.

V Brain-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.

Type of Use (Select one or both, as applicable)
☑ Prescription Use (Part 21 CFR 801 Subpart D)	☐ Over-The-Counter Use (21 CFR 801 Subpart C)

CONTINUE ON A SEPARATE PAGE IF NEEDED## CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

==============================================================================================================================================================================

{3}------------------------------------------------

Image /page/3/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized purple "V" shape above the word "VYSIONEER" in a sans-serif font. A horizontal purple line is located below the word "VYSIONEER".

Section 5 510(k) Summary K212116

5.1 Submitter

Vysioneer Inc.

33 Rogers St. #308, Cambridge, MA 02142

Contact Person:	Vicki Lin (Regulatory Specialist)
Phone:	609-865-8659
Email:	vicki.lin@vysioneer.com
Date Summary Prepared:	September 10, 2021

5.2 Device Name

Trade Name:	VBrain-OAR
Common Name:	Radiological Image Processing Software forRadiation Therapy
Regulation Number / Product Code:	21 CFR 892.2050 / QKB

5.3 Predicate Device

Primary Predicate Device: AccuContour™

510(k) Holder/Submitter: Xiamen Manteia Technology LTD.

510(k) Number: K191928 (Cleared on 02/28/2020)

Reference Device: iPlan

510(k) Holder/Submitter: Brainlab AG

510(k) Number: K113732 (Cleared on 05/07/2012)

{4}------------------------------------------------

Image /page/4/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of the word "Vysioneer" in a sans-serif font, with the letters in a dark blue color. Above the word is a stylized "V" shape, also in dark blue, formed by two angled lines that do not quite meet at the bottom. The overall design is simple and modern.

5.4 Intended Use / Indications for Use

VBrain-OAR is a software device intended to assist trained radiotherapy personnel including, but not limited to, radiologists, radiation oncologists, neurosurgeons, radiation therapists, dosimetrists, 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 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 be used 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 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.

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 viewing/editing functions, such as image visualization software and treatment planning system.

Device Description ર્સ્ડ

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.

{5}------------------------------------------------

Image /page/5/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized letter 'V' in purple, with the word 'VYSIONEER' written in a sans-serif font below it. A thin purple line is located under the word 'VYSIONEER'.

5.6 Comparison of Technological Characteristics with the Predicate Device

VBrain-OAR is substantially equivalent to the primary predicate device AccuContour™ (K191928).

The proposed device, VBrain-OAR, and the primary predicate, AccuContour™ (K191928) are both AI-based (deep learning) software devices intended to be used in the workflow of radiation therapy treatment planning by providing tools to automatically segment/contour organs at risk on images as well as perform image registration (image fusion). In addition, VBrain-OAR and the predicate device both use an intensity-based algorithm for image registration. Both the proposed device and AccuContour™ (K191928) are regulated under 21 CFR 892.2050, Product Code QKB (Radiological Image Processing Software for Radiation Therapy).

Both the proposed device and the reference device, iPlan (K113732), are designed to contour organs at risk of brain on MR images and perform image registration (image fusion). The only difference between these two software devices is that VBrain-OAR uses an AI-based algorithm, while iPlan uses an atlas-based algorithm for segmentation of images.

Please see Table 5-1 comparing the intended use and key technological characteristics of VBrain-OAR and the predicate and reference device.

	Proposed Device	Primary PredicateDevice	Reference Device
Company	Vysioneer Inc.	Xiamen ManteiaTechnology LTD.	Brainlab AG
Device Name	VBrain-OAR	AccuContour™	iPlan
510k Number	K212116	K191928	K113732
Regulation No.	21CFR 892.2050	21CFR 892.2050	21CFR 892.1750
Classification	II	II	II
Product Code	QKB	QKB	JAK/LLZ
IntendedUse/Indication for Use	VBrain-OAR is asoftware device intendedto assist trainedradiotherapy personnel	It is used by radiationoncology department toregister multimodalityimages and segment(non-contrast) CT	iPlan's indications foruse are the viewing,presentation anddocumentation ofmedical imaging,
Proposed Device	Primary PredicateDevice	Reference Device
including, but notlimited to, radiologists,radiation oncologists,neurosurgeons, radiationtherapists, dosimetrists,and medical physicists,during their clinicalworkflows of braintumor radiation therapytreatment planning, byproviding initial objectcontours of organs atrisk in the brain (i.e., theregion of interest, ROI)on axial T1 contrast-enhanced brain MRIimages. VBrain-OAR isintended to be used onadult patients only.VBrain-OAR uses anartificial intelligencealgorithm (i.e., deeplearning neuralnetworks) to contour(segment) organs at risk(brain stem, eyes, opticnerves, optic chiasm) inthe brain on MRI imagesfor trained radiotherapypersonnel's attention,which is meant forinformational purposesonly and not intendedfor replacing theircurrent standard practiceof manual contouringprocess. VBrain-OARdoes not alter theoriginal MRI image, nordoes it intend to be usedto detect tumors fordiagnosis. VBrain-OARis intended only forcontouring andgenerating contours oforgans at risk in the	images, to generateneeded information fortreatment planning,treatment evaluation andtreatment adaptation.The product has twoimage process functions:(1) Deep learningcontouring: it canautomatically contourthe organ-at-risk,including head and neck,thorax, abdomen andpelvis (for both male andfemale),(2) AutomaticRegistration, and(3) Manual Contour.It also has the followinggeneral functions:(1) Receive,add/edit/delete, transmit,input/export, medicalimages and DICOMdata;(2) Patient management;(3) Review of processedimages;(4) Open and Save offiles.	including differentmodules for imageprocessing, imagefusion, atlas assistedvisualization andsegmentation,intraoperative functionalplanning where theoutput can be used e.g.with stereotactic imageguided surgery or otherdevices for furtherprocessing andvisualization.Example proceduresinclude but are notlimited to:Planning andsimulation of cranialsurgical procedures suchas tumor resection, shuntplacement, minimal-invasive stereotacticinterventions, biopsy,planning and simulationof trajectories forstimulation and electroderecording ENT proceduressuch as sinus surgery,tumor surgery Spineprocedures such astumor surgery, pediclescrew planning,vertebroplasty planning Plan View is anapplication which isintended to be used forreviewing existingtreatment plans Planning andsimulation of cranio-maxillofacial proceduresTypical users of iPlanare medical
	Proposed Device	Primary PredicateDevice	Reference Device
	brain; it is not intendedto be used with imagesof other body parts.		professionals, includingbut not limited tosurgeons andradiologists.
	VBrain-OAR alsocontains the automaticimage registrationfeature to registervolumetric medicalimage data. (e.g., MR,CT). It allows rigidimage registration toadjust the spatialposition and orientationof two images.
	Radiation therapytreatment personnelmust finalize (confirm ormodify) the contoursgenerated by VBrain-OAR, as necessary,using an externalplatform available at thefacility that supportsDICOM-RTviewing/editingfunctions, such as imagevisualization softwareand treatment planningsystem.
Device Description	VBrain-OAR is asoftware applicationsystem indicated for usein the contouring(segmentation) of brainMRI images for theorgans at risk (OAR) inthe brain duringradiation treatmentplanning and in theregistration of multi-modality images. Thedevice consists of 2algorithm modules,which are contouring	The proposed device,AccuContour, is astandalone softwarewhich is used byradiation oncologydepartment to registermultimodality imagesand segment (non-contrast) CT images, togenerate neededinformation fortreatment planning,treatment evaluation andtreatment adaptation.	iPlan is a software basedtreatment planningapplication providingfunctionalities likeviewing, processing anddocumentation ofmedical data includingdifferent modules forimage preparation,image fusion, imagesegmentation where theresult is a treatment planthat can be used e.g. forstereotactic and/or imageguided surgery.
Proposed Device	Primary PredicateDevice	Reference Device
algorithm module andregistration algorithmmodule, and a workflowmanagement module.The modules can workindependently, and yetcan be integrated witheach other.The contouring(segmentation)algorithm moduleconsists of imagepreprocessing, deeplearning neuralnetworks, andpostprocessingcomponents, and isintended to contourorgans at risk in thebrain on the axial T1contrast-enhanced MRimages. It utilizes deeplearning neural networksto generate contours forthe organs at risk in thebrain and export theresults as DICOM-RTobjects (using the RTStructure Set ROIContour attribute,RTSTRUCT).The registrationalgorithm moduleregisters volumetricmedical image data (e.g.,MR, CT). It allows rigidimage registration toadjust the spatialposition and orientationof two images.The workflowmanagement module isconfigured to work on aPACS network. Upon	The product has twoimage process functions:(1) Deep learningcontouring: it canautomatically contourthe organ-at-risk,including head and neck,thorax, abdomen andpelvis (for both male andfemale),(2) AutomaticRegistration, and(3) Manual Contour.It also has the followinggeneral functions:Receive, add/edit/delete,transmit, input/export,medical images andDICOM data;Patient management;Review of processedimages;Open and Save of files.
	Proposed Device	Primary PredicateDevice	Reference Device
	user's request, it willpull patient scans orusers can sendcorresponding DICOMimages, and it willtrigger a predefinedworkflow, in whichdifferent algorithmmodules are executed togenerate the DICOMoutput. The DICOMoutput of a workflow aresent back to the PACS.
Segmentation(Contouring)Technology	Deep learning	Deep learning	Atlas-based
Operating System	Linux operating system	Microsoft Windows	Microsoft Windows
User Population	Trained radiotherapypersonnel including, butnot limited to,radiologists, radiationoncologists, physicians,dosimetrists, andmedical physicists.	It is used by radiationoncology department.	Typical users of iPlanare medicalprofessionals, includingbut not limited tosurgeons andradiologists.
Supported Modalities	Segmentation Featuresfor organs at risk in thebrain: axial T1 contrast-enhanced MR images.Registration Features:CT, MR	Segmentation Features:Non-Contrast CTRegistration Features:CT, MRI, PET	CT, MRI, PET andSPECT
Image segmentation:Localization andDefinition of Objects(ROI)	Organs at risk in thebrain	Organ-at-risk, includinghead and neck, thorax,abdomen and pelvis (forboth male and female)	Outline anatomicalstructures using manualor automaticsegmentation methods.Advanced manipulationfor 3D objects withscaling, logicaloperations and objectsplitting.
Image RegistrationAlgorithm	Intensity-based	Intensity-based	Intensity-based
	Proposed Device	Primary PredicateDevice	Reference Device
Alteration of OriginalImages	No	No	No

Table 5-1. Comparison with the Predicate and Reference Devices.

{6}------------------------------------------------

Image /page/6/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of the word "VYSIONEER" in a sans-serif font, with a stylized "V" shape above it. The color of the logo is a dark shade of purple.

{7}------------------------------------------------

Image /page/7/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of the word "VYSIONEER" in a sans-serif font, with a stylized "V" shape above it. The color of the logo is a dark purple.

{8}------------------------------------------------

Image /page/8/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized letter V above the word "VYSIONEER". The letter V is purple and is made up of two diagonal lines that do not quite meet at the bottom. The word "VYSIONEER" is also purple and is in all caps.

{9}------------------------------------------------

Image /page/9/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of the word "VYSIONEER" in a sans-serif font, with a horizontal line above and below the word. Above the word is a stylized "V" shape, which is made up of two diagonal lines that do not quite meet at the top. The color of the logo is a dark purple.

{10}------------------------------------------------

Image /page/10/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized letter 'V' in purple, with a small gap in the upper left corner. Below the symbol, the word "VYSIONEER" is written in a sans-serif font, also in purple. A thin purple line is located below the word.

5.7 Non-Clinical Test (Standalone Performance Data)

Standalone performance testing was conducted to evaluate the contouring (segmentation) performance and registration performance of VBrain-OAR. VBrain-OAR was tested on datasets from multiple institutions.

The segmentation testing demonstrated that the auto-segmentation algorithm of the VBrain-OAR algorithm module provides clinically acceptable contours for organs at risk in the brain structures on an image of a patient.

The registration testing evaluated the quality of the rigid registration of the registration algorithm module on images of multiple modalities, to demonstrate substantial equivalence to the predicate device.

Stratified analysis of both testing showed consistent performance on data across patient sex, multiple imaging hardware and protocols.

Software Verification and Validation 5.8

Software verification and validation testing were conducted, and documentation was provided in accordance with FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" for software devices identified as Major Level of Concern related to radiation therapy treatment planning.

રું તે તે Substantially Equivalent (SE) Conclusion

Vysioneer Inc. has conducted performance testing on VBrain-OAR. In all the cases, the software showed clinically acceptable performance. Verification and validation testing and hazard analysis demonstrate that VBrain-OAR performs within its design specifications and is as safe and effective as the predicate. The minor technological differences between VBrain-OAR and the predicate device with regard to the intended use do not introduce any new potential risks. Based on the information presented in these 510(k) premarket notifications, VBrain-OAR is considered substantially equivalent to the predicate device.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).