Who is the manufacturer of AccuBrain?

Brainnow Medical Technology Limited submitted the Traditional clearance for AccuBrain (K202847).

What is the FDA product code for AccuBrain?

LLZ. It is classified under 21 CFR 892.2050 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for AccuBrain?

510(k) clearance (submission type: Traditional).

What are the predicate devices for AccuBrain?

NeuroQuant (K170981); NeuroReader Medical Image Processing Software (K140828).

AccuBrain

K202847 · Brainnow Medical Technology Limited · LLZ · Jan 15, 2021 · Radiology

Device Facts

Record ID	K202847
Device Name	AccuBrain
Applicant	Brainnow Medical Technology Limited
Product Code	LLZ · Radiology
Decision Date	Jan 15, 2021
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	Software as a Medical Device

Intended Use

AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs and returns an analysis report.

Device Story

AccuBrain is a fully automated post-processing software for 3D T1-weighted MRI scans; used by clinicians (radiologists, neurologists) in clinical research and routine care. Input: 3D T1 MRI series. Processing: automated pipeline including artifact correction (non-local mean filtering, N4 bias correction, histogram matching intensity normalization), multi-atlas-based segmentation (atlas pool of 300 brain MRIs), and STAPLE label fusion. Output: morphometric analysis report (PDF) with hippocampal volumes and segmented color overlays. Device operates on off-the-shelf hardware; supports DICOM. Benefits: provides objective volumetric quantification to support clinical assessment of structural MRIs.

Clinical Evidence

Bench testing only. Accuracy evaluated against manual segmentation using 135 EADC-ADNI HarP datasets; mean Dice coefficient 0.89 (std 0.03) for right, left, and total hippocampus. Reproducibility evaluated via intrascanner Coefficient of Variation (CV): 3.20% (left) and 1.23% (right). Mean percentage absolute volume differences were 4.52% (left) and 1.74% (right).

Technological Characteristics

Software-based image processing; operates on off-the-shelf hardware. Uses multi-atlas-based segmentation with STAPLE label fusion. Input: 3D T1W MRI (DICOM). Connectivity: standalone/networked. Software lifecycle: ANSI AAMI IEC 62304:2006/A1:2016 compliant. Algorithms: non-local mean filtering, N4 bias correction, histogram matching, STAPLE label fusion.

Indications for Use

Indicated for automatic labeling, visualization, and volumetric quantification of the hippocampus from T1-weighted MRI scans in patients, including those with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI), as well as healthy subjects.

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

NeuroQuant (K170981)
NeuroReader Medical Image Processing Software (K140828)

Related Devices

K170981 — NeuroQuant · Cortechs Labs, Inc. · Sep 7, 2017
K193287 — CorInsights MRI · Adm Diagnostics, Inc. · Nov 20, 2020
K220437 — Neurophet AQUA · Neurophet., Inc. · May 10, 2023
K140828 — NEUROREADER MEDICAL IMAGE PROCESSING SOFTWARE · Brainreader Aps · Feb 4, 2015
K171328 — cNeuro cMRI · Combinostics OY · Jan 8, 2018

Submission Summary (Full Text)

{0}------------------------------------------------ Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue. BrainNow Medical Technology Limited % You Yijie General Manager Qimmiq Medical Consulting Service Co., Ltd. RM.1711, Building K. NO.101 Science Ave International Creative Valley Guangzhou, Guangdong 510663 CHINA January 15, 2021 Re: K202847 Trade/Device Name: AccuBrain Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: September 11, 2020 Received: December 11, 2020 Dear You Yijie: 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/cfpmn/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 {1}------------------------------------------------ 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 https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) 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 mediation-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) K202847 Device Name AccuBrain Indications for Use (Describe) AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of MRIs and returns an analysis report. 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. 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}------------------------------------------------ # 510(k) Summary ## "510(k) Summary" as required by 21 CFR Part 807.92. # 1. Submitter's Information ## Establishment Registration Information Name: BrainNow Medical Technology Limited Address: Unit 201, 2/F, Lakeside 2, No. 10 Science Park West Avenue, Hong Kong Science Park, Shatin, N.T., Hong Kong ZIP/Postal Code: 999077 #### Contact Person of applicant Contact Person: Junbing Huang Telephone Number: 86-13416183887 Fax Number: 852-36221760 Email: 417731983@qq.com #### Contact Person of the Submission: Name: You Yijie Address: RM.1711, Building K, NO.101 Science Ave International Creative Valley Development Zone, Guangzhou China TEL: +86 020-8224 5821 FAX: +86 020-8224 5821 Email: Jet.you@qimmig-med.com Date to prepare: 9/11/2020 ## 2. Device Information Device Name: AccuBrain Common Name: Neuroimage Analysis Software Model: AccuBrain_Intl Software version: V1.0.0.200703 Classification Name: System, Image Processing, Radiological Regulation Number: 21 CFR 892.2050 Regulation Description: Picture archiving and communications system Product Code: LLZ Classification Panel: Radiology Regulation Class: - #### 3. Predicate Device Information | Item | Primary Predicate(A) | Predicate or Reference Device (B) | |-------------------------|--------------------------------------|--------------------------------------------------| | 510(k) submitter/holder | CorTechs Labs, Inc | Brainreader Aps | | 510(K) Number | K170981 | K140828 | | Device name | NeuroQuant | NeuroReader Medical Image Processing Software | | Common name | Medical Image Processing Software | Neuroreader | {4}------------------------------------------------ | Regulation Description | Picture archiving and communications system | Picture archiving and communication system | |------------------------|------------------------------------------------|-----------------------------------------------| | Review panel | Radiology | Radiology | | Product code | LLZ | LLZ, LNH | | Regulation Class | Class II | Class II | | Regulation Number | 21 CFR 892.2050 | 21 CFR 892.2050 | # 4. Device Description AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs and returns an analysis report. The resulting output is a morphometric report in PDF format. The software is suitable for use in both clinical trial research and routine patient care as a support tool for clinicians in assessment of structural MRIs. AccuBrain provides morphometric measurements based on 3D T1 MRI series. The output of the software includes volumes of hippocampus. The AccuBrain processing architecture includes an automated internal pipeline that performs artifact correction, segmentation, hippocampus quantification, volume calculation and report generation. Additionally, automated safety measures include automated quality control functions, such as DICOM check, age check and image resolution check and image quality check. #### 5. Principle of operation AccuBrain automatically segmented the subject's hippocampi using the uploaded T1W MRIs in a multi-atlas-based segmentation manner. The quantification and visualization of hippocampal segmentation results were output in the form of a morphometric analysis report. The hippocampal segmentation procedure is described as follows. 1) Pre-processing to increase the image quality, including noise reduction, bias field correction, and intensity normalization to normalize intensity level of MRIs from different scanners. For noise reduction method, we used non-local mean filtering method [1]. Bias correction method used in AccuBrain is N4 bias correction [2]. Intensity Normalization method used in AccuBrain is histogram matching [3]. 2) Atlas selection. The atlas pool, consisting of 300 brain MRIs together with their segmentation labels, were previously obtained from different individuals using different scanners and have highly variable appearances. Each atlas contains both brain MRI and the prior encoded radiologist-specified anatomy information for hippocampus. The detailed of demographic information about the atlas data was described in Table 1. During processing, AccuBrain selects a number of brain images from the atlas pool based on similarity with the subject images. The similarity measures used in this step is Normalized Cross correlation (NCC). AccuBrain will select 10 images from the atlas pool with highest NCC scores. 3) Image segmentation. The non-rigid image registration is performed to match the selected image with the subject image. The resulting transformation field will be applied to transform the predefined atlas label to the subject image. As 10 template images are selected, 10 segmentation results are obtained and will be merged using STAPLE label fusion method l41 to fuse the final segmentation labels of the subject image. {5}------------------------------------------------ | Demographic Categories | Frequency (subject number) | Percentage(%) | |-------------------------|-------------------------------|---------------| | Gender | | | | Female | 151 | 50.3 | | Male | 149 | 49.7 | | Disease Status | | | | AD | 123 | 41 | | NC | 115 | 38.3 | | MCI | 62 | 20.7 | | Age | | | | 51-60 | 25 | 8.3 | | 61-70 | 96 | 32 | | 71-80 | 118 | 39.3 | | 81-90 | 61 | 20.3 | | Magnetic Field Strength | | | | 1.5T | 68 | 22.7 | | 3T | 232 | 77.3 | | Manufacturer | | | | GE | 131 | 43.7 | | Philips | 44 | 14.7 | | Siemens | 125 | 41.7 | | In-plane resolution | | | | 1x1 | 214 | 71.3 | | 0.9375x0.9375 | 84 | 28 | | 0.8594x0.8594 | 2 | 0.7 | | FOV (mm²) | | | | 220 | 2 | 0.7 | | 230 | 135 | 45 | | 240 | 84 | 28 | | 256 | 79 | 26.3 | | Slice Thickness(mm) | | | | 1 | 216 | 72 | | 1.2 | 84 | 28 | # Table 1. Demographic information of the atlas data #### 6. Indications for Use AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs and returns an analysis report. # 7. Comparison of Predicate Devices Summary Comparison Table for the subject device and predicate devices (K170981 and K140828): | Comparison Elements | Subject Device | Predicate Device (A) | Predicate or Reference Device (B) | Discussion of difference | |------------------------|----------------|-------------------------|-----------------------------------------|-----------------------------| | Device Name | AccuBrain | NeuroQuant | NeuroReader | / | | 510(k) No | / | K170981 | K140828 | / | {6}------------------------------------------------ | Regulation No | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.2050 | Same | | | | | according to the ANSI AAMI IEC 62304:2006/A1:2016 (Software Documentation, section 004) and the Accuracy and Reproducibility of the Subject Device was verified (section 006). The difference does not affect the determination of substantial equivalence. | |------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|------------------------------------------------------------------------------|------------------------------------------------------------------------------|------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Regulation Description | "Picture archiving and communications system" | "Picture archiving and communications system" | "Picture archiving and communications system" | Same | Processing architecture | Automated internal pipeline that performs: | Automated internal pipeline that performs: | Information not publicly available | SE-within the predicate | | Classification name | System, Image Processing, Radiological | System, Image Processing, Radiological | System, Image Processing, Radiological | Same | | -artifact correction | -artifact correction | | Both are performed: | | Classification | Class II | Class II | Class II | Same | | -segmentation | -segmentation | | -artifact correction | | Product code | LLZ | LLZ | LLZ | Same | | -hippocampus quantification | -lesion quantification | | -segmentation | | Indications for use | AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs and returns an analysis report. | Automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric data may be compared to reference percentile data | Automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. This software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images. | SE-within the predicate Both are indicated to automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs Is and returns an analysis report. | | -volume calculation | -volume calculation | | -hippocampus quantification | | Design and incorporated technology | • Automated measurement of hippocampus volumes • Automatic atlas-based segmentation and quantification of hippocampus using an atlas pool consisting of difference | • Automated measurement of brain tissue volumes and structures and lesions • Automatic segmentation and quantification of brain structures using a dynamic probabilistic | Information not publicly available | SE Both are indicated to automate measurement of hippocampus volumes through automatic atlas-based segmentation and quantification of hippocampus base on an atlas pool consisting of difference template | | -report generation | -report generation | | -volume calculation | | template images with highly variable appearance together with their prior encoded radiologist- specified anatomy information | images with highly variable appearance together with their prior encoded radiologist-specified anatomy information | neuroanatomical atlas, with age and gender specificity, based on the MR image intensity | Information not publicly available | images with highly variable appearance together with their prior encoded radiologist-specified anatomy information. The difference does not affect the determination of substantial equivalence. | | | | | -report generation | | Physical characteristics | • Web-based application • Operates on off-the -shelf hardware (multiple vendors) | • Software package • Operates on off-the-shelf hardware (multiple vendors) | Information not publicly available | SE The Cybersecurity of Subject Device was verified (Cybersecurity Information Document, section 005). And the Subject Device was verified according to the ANSI AAMI IEC 62304:2006/A1:2016 (section 004) and the Accuracy and Reproducibility of the Subject Device was verified (section 006). The difference does not affect the determination of substantial equivalence. | | | | | The difference does not affect the determination of substantial equivalence. | | Operating system | Supports Windows | Supports Linux, Mac OS X and Windows. | Information not publicly available | SE--within the predicate. The Operating system of Subject Device is fewer than Predicate Device (A), the risk of the Subject Device is fewer than Predicate Device (A). And the Subject Device was verified | Data source | • MRI scanner: 3D T1 MRI scans acquired with specified protocols | • MRI scanner: 3D T1 MRI scans acquired with specified protocols | Information not publicly available | Same | | | • AccuBrain supports DICOM format as input | • NeuroQuant supports DICOM format as input | | | | | | | | | Output | -Provides volumetric | • Provides volumetric measurements | Information not publicly available | SE-within the predicate | | | | | | {7}------------------------------------------------ {8}------------------------------------------------ {9}------------------------------------------------ | | measurements of hippocampus -Includes segmented color overlays and an analysis report (just volumetric of the hippocampus) | of brain structures and lesions • Includes segmented color overlays and morphometric reports • Automatically compares results to reference percentile data and to prior scans when available • Supports DICOM format as output of results that can be displayed on DICOM workstations and Picture Archive and Communications Systems | | Both provide volumetric measurements of hippocampus -Includes segmented color overlays and an analysis report including the volumetric of the hippocampus. The difference does not affect the determination of substantial equivalence. | |----------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Accuracy | The mean DICE of AccuBrain results and manual segmentation results is 0.89, 0.89 and 0.89 for right, left and total hippocampus, respectively. | For major subcortical brain structures Dice's coefficients are in the range of 80%-90%. | NeuroReader can segment the hippocampus with a Dice similarity index of 0.87 for both the right and left hippocampus. | SE The accuracy and reproducibility of hippocampus segmentation of AccuBrain with T1W MRI images are comparable with Predicate Device (A) and Predicate Device (B). The accuracy and reproducibility of Subject Device was verified (Accuracy and Reproducibility Test Report, section 006). | | | | | | The difference does not affect the determination of substantial equivalence. | | Safety | • Automated quality control functions - DICOM check - Age check - Image resolution check - Image quality check Diagnostic decisions should be made by trained clinicians. | • Automated quality control functions - Tissue contrast check - Scan protocol verification - Atlas alignment check • Results must be reviewed by a trained physician | Information not publicly available | SE Both are conducted the - DICOM check, Age check, Image resolution check, Image quality check. The Cybersecurity of Subject Device was verified (Cybersecurity Information Document, section 005). And the Subject Device was verified according to the IEC 62304(Software Documentation, section 004) and the Accuracy and Reproducibility of the Subject Device was verified (Accuracy and Reproducibility test report, section 006). The difference does not affect the determination of substantial equivalence. | {10}------------------------------------------------ Subject device and predicate devices are softwares for automatically identifying and quantifying volumes of brain structures. Subject and predicate devices take 3D MR images of the brain as input and generate electronic report with similar quantitative information. AccurBrain and NeuroQuant achieve the intended use based on similar principle and processing architecture, since the quantification systems implement brain segmentation and quantification using atlas-based segmentation scheme. Both hippocampi are segmented and the volumes are calculated. {11}------------------------------------------------ AccurBrain and NeuroQuant are DICOM compatible and operate on off-the-shelf hardware. Meanwhile, both devices are used by medical professional, such as radiologists, neurologists and neuroradiologists, as well as by clinical researchers, as a support tool in assessment of structural MRIs. The output volumes which both devices provide include volumes of left hippocampus, right hippocampus and whole hippocampi. # 8. Performance Testing To demonstrate the performance of AccuBrain (model: AccuBrain_Intl), the measured volumes and volume differences of hippocampus are validated for accuracy and reproducibility. The subjects upon whom the device was tested include healthy subjects, Alzheimer's disease patients and Mild Cognitive Impairment patients. AccuBrain segmentation accuracy with 3D T1 MRI scans was evaluated using Dice coefficient metric. With 135 data provided by the EADC-ADNI HarP, the mean Dice coefficient by comparing AccuBrain results and manual segmentation results was 0.89 (std: 0.03), 0.89 (std: 0.03) and 0.89 (std: 0.03) for right, left and total hippocampal volumes, respectively. Segmentation reproducibility of repeated 3D T1 MRI scans for the same subjects was evaluated using Coefficient of Variation (CV). The mean intrascanner CV values were 3.20% and 1.23%, the mean percentage absolute volume differences DIFF values were 4.52% and 1.74% for left and right hippocampus, respectively. Compared with the performances of the predicate devices, the results presented above shows that the subject device is safe and effective and performs as well as the predicate devices. The AccuBrain (Model: AccuBrain_Intl) was designed, verified, and validated according to the company's Design Control process and has been subjected to extensive safety and performance testing as shown in the test results provided in this submission. Verification and Validation testing data demonstrate that the device meets all of its specifications. The Accuracy and Reproducibility of AccuBrain (Model: AccuBrain Intl) was verified please see section 006 for the Accuracy and Reproducibility Test Report. The software of AccuBrain (Model: AccuBrain Intl) was verified according to the ANSI AAMI IEC 62304:2006/A1:2016 Medical device software - Software life cycle processes [Including Amendment 1 (2016)]. Please see section 004 for the Software Documentation. The Cybersecurity of AccuBrain (Model: AccuBrain Intl) was verified. Please see section 005 for the Cybersecurity Information Document. During the verification and validation activity the following quidance documents were used: General Principles of Software Validation: Guidance for Industry and FDA Staff Postmarket Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff Content of Premarket Submissions for Management of Cybersecurity in Medical Devices: Draft Guidance for Industry and Food and Drug Administration Staff Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices: Guidance for Industry and FDA Staff #### 9. Conclusions The performance testing presented above shows that the device is as safe, as effective and performs as well as the predicate devices(A) and predicate devices(B), {12}------------------------------------------------ and as well as gold standard-computer-aided expert manual segmentation. By virtue of the physical characteristics and intended user, AccuBrain(AccuBrain_Intl) is substantially equivalent to its predicate devices (A) (K170981) and predicate devices (B)(K140828). # 10. Bibliography [1] Coll, Bartomeu & Morel, Jean-Michel. (2005). A non-local algorithm for image denoising. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2. 60- 65 vol. 2. [2] Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310-1320. [3] Laszlo G. Nyul, Jayaram K. Udupa, and Xuan Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Transactions on Medical Imaging, 19(2):143-150, 2000. [4] Warfield SK, et al. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. Medical Imaging, IEEE Transactions on. 2004;23:903-921.