It is used by radiation oncology department to register multi-modality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.

Device Description

The proposed device, AccuContour, is a standalone software which is used by radiation oncology department to register multi-modality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.

The product has two image processing functions:

(1) Deep learning contouring: it can automatically contour organs-at-risk, in head and neck, thorax, abdomen and pelvis (for both male and female) areas,
(2) Automatic registration: rigid and deformable registration, and
(3) Manual contouring.

It also has the following general functions:

Receive, add/edit/delete, transmit, input/export, medical images and DICOM data;
Patient management;
Review of processed images;
Extension tool;
Plan evaluation and plan comparison;
Dose analysis.

AI/ML Overview

This document (K221706) is a 510(k) Premarket Notification for the AccuContour device by Manteia Technologies Co., Ltd. It declares substantial equivalence to a predicate device and several reference devices. The focus here is on the performance data related to the "Deep learning contouring" feature and the "Automatic registration" feature.

Based on the provided document, here's a detailed breakdown of the acceptance criteria and the study proving the device meets them:

I. Acceptance Criteria and Reported Device Performance

The document does not explicitly provide a clear table of acceptance criteria and the reported device performance for the deep learning contouring in the format requested. Instead, it states that "Software verification and regression testing have been performed successfully to meet their previously determined acceptance criteria as stated in the test plans." This implies that internal acceptance criteria were met, but these specific criteria and the detailed performance results (e.g., dice scores, Hausdorff distance for contours) are not disclosed in this summary.

However, for the deformable registration, it provides a comparative statement:

Feature	Acceptance Criteria (Implied)	Reported Device Performance
Deformable Registration	Non-inferiority to reference device (K182624) based on Normalized Mutual Information (NMI)	The NMI value of the proposed device was non-inferior to that of the reference device.

It's important to note:

For Deep Learning Contouring: No specific performance metrics or acceptance criteria are listed in this 510(k) summary. The summary only broadly mentions that the software "can automatically contour organs-at-risk, in head and neck, thorax, abdomen and pelvis (for both male and female) areas." The success is implicitly covered by the "Software verification and validation testing" section.
For Automatic Registration: The criterion is non-inferiority in NMI compared to a reference device. The specific NMI values are not provided, only the conclusion of non-inferiority.

II. Sample Size and Data Provenance

Test Set (for Deformable Registration):
- Sample Size: Not explicitly stated as a number, but described as "multi-modality image sets from different patients."
- Data Provenance: "All fixed images and moving images are generated in healthcare institutions in U.S." This indicates prospective data collection (or at least collected with the intent for such testing) from the U.S.
Training Set (for Deep Learning Contouring):
- Sample Size: Not explicitly stated in the provided document.
- Data Provenance: Not explicitly stated in the provided document.

III. Number of Experts and Qualifications for Ground Truth

For the Test Set (Deformable Registration): The document does not mention the use of experts or ground truth establishment for the deformable registration test beyond the use of NMI for "evaluation." NMI is an image similarity metric and does not typically require human expert adjudication of registration quality in the same way contouring might.
For the Training Set (Deep Learning Contouring): The document does not specify the number of experts or their qualifications for establishing ground truth for the training set.

IV. Adjudication Method for the Test Set

For Deformable Registration: Not applicable in the traditional sense, as NMI is an objective quantitative metric. There's no mention of human adjudication for registration quality here.
For Deep Learning Contouring (Test Set): The document notes there was no clinical study included in this submission. This implies that if a test set for the deep learning contouring was used, its ground truth (and any adjudication process for it) is not described in this 510(k) summary. Given the absence of a clinical study, it's highly probable that ground truth for performance evaluation of deep learning contouring was established internally through expert consensus or other methods, but details are not provided.

V. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Was it done?: No, an MRMC comparative effectiveness study was not reported. The document explicitly states: "No clinical study is included in this submission."
Effect Size: Not applicable, as no such study was performed or reported.

VI. Standalone (Algorithm Only) Performance Study

Was it done?: Yes, for the deformable registration feature. The NMI evaluation was "on two sets of images for both the proposed device and reference device (K182624), respectively." This is an algorithm-only (standalone) comparison.
For Deep Learning Contouring: While the deep learning contouring is a standalone feature, the document does not provide details of its standalone performance evaluation (e.g., against expert ground truth). It only states that software verification and validation were performed to meet acceptance criteria.

VII. Type of Ground Truth Used

Deformable Registration: The "ground truth" for the deformable registration evaluation was implicitly the images themselves, with NMI being used as a metric to compare the alignment achieved by the proposed device versus the reference device. It's an internal consistency/similarity metric rather than a "gold standard" truth established by external means like pathology or expert consensus.
Deep Learning Contouring: Not explicitly stated in the provided document. Given that it's an AI-based contouring tool and no clinical study was performed, the ground truth for training and internal testing would typically be established by expert consensus (e.g., radiologist or radiation oncologist contours) or pathology, but the document does not specify.

VIII. Sample Size for the Training Set

Not explicitly stated in the provided document for either the deep learning contouring or the automatic registration.

IX. How Ground Truth for the Training Set was Established

Not explicitly stated in the provided document for either the deep learning contouring or the automatic registration. For deep learning, expert-annotated images are the typical method, but details are absent here.

Summary

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

Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the FDA acronym with the full name of the agency on the right. The symbol on the left is a stylized representation of a human figure, while the FDA acronym is in a blue square. The full name of the agency, "U.S. Food & Drug Administration," is written in blue letters next to the acronym.

Manteia Technologies Co., Ltd. % Dandan Chen RA 1903, B Tower, Zijin Plaza No. 1811 Huandao East Road Xiamen, 361001 CHINA

Re: K221706

Trade/Device Name: AccuContour Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QKB Dated: Mav 31, 2022 Received: June 13, 2022

Dear Dandan Chen:

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

March 9, 2023

{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,

Image /page/1/Picture/5 description: The image shows a digital signature. The signature is for Lora D. Weidner. The date of the signature is March 9th, 2023. The time of the signature is 19:17:29 -05'00'.

Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Ouality Center for Devices and Radiological Health

Enclosure

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

Indications for Use

510(k) Number (if known) K221706

Device Name AccuContour

Indications for Use (Describe)

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

I . SUBMITTER

Manteia Technologies Co., Ltd. 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen

China Establishment Registration Number: 3016686005

Contact Person: Dandan Chen Position: RA Tel: +86 592-6100813 Email: chendandan@manteiatech.com

Date Prepared: March 9, 2023

II . DEVICE

Name of Device: AccuContour Common or Usual Name: Medical Imaging Software Classification Name: System, Imaging processing, Radiological Regulatory Class: II Product Code: QKB Regulation Number: 21CFR 892.2050 Review Panel: Radiology

III. PREDICATE DEVICE

Device	510(k) Number	Product Name
Predicate Device	K191928	AccuContour™
Reference Device	K182624	MIM-MRT Dosimetry
Reference Device	K173636	Velocity
Reference Device	K181572	Workflow Box

IV. DEVICE DESCRIPTION

The product has two image processing functions:

(1) Deep learning contouring: it can automatically contour organs-at-risk, in head and neck,

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

thorax, abdomen and pelvis (for both male and female) areas,

(2) Automatic registration: rigid and deformable registration, and
(3) Manual contouring.

It also has the following general functions:

Receive, add/edit/delete, transmit, input/export, medical images and DICOM data;
Patient management;
Review of processed images;
► Extension tool;
Plan evaluation and plan comparison;
► Dose analysis.

V . INDICATIONS FOR USE

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

VI. SUBSTANTIALLY EQUIVALENT (SE) COMPARISION

Table 1 Comparison of Technology Characteristics

ITEM	Proposed Device	Predicate DeviceK191928	Reference DeviceK182624	Reference DeviceK173636	Reference DeviceK181572
Regulatory Information
Regulation No.	21CFR 892.2050	21CFR 892.2050	21CFR 892.2050	21CFR 892.2050	21CFR 892.2050
Product Code	QKB	QKB	LLZ	LLZ	LLZ
Indication for Use	It is used by radiation oncology department to register multi-modality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.	It is used by radiation oncology department to register multi-modality images and segment (non-contrast) CT information for treatment planning, treatment evaluation and treatment adaptation.	MIM software is used by trained medical professionals as a tool to aid in evaluation and information management of digital medical images. The medical image modalities include, but are not limited to, CT, MRI, CR, DX, MG, US, SPECT, PET and XA as supported by ACR/NEMA DICOM 3.0. MIM assists in the following indications:Receive, transmit, store, retrieve, display, print, and process medical images and DICOM objects.Create, display and print reports from medical images.	Velocity is a software package that provides the physicians a means for comparison of medical data including imaging data that is DICOM compliant.It allows the display, annotation, volume operation, volume rendering, registration, and fusion of medical images as an aid during use by diagnostic radiology, oncology, radiation therapy planning and other medical specialties. Velocity is not intended for mammography.	Workflow Box is a software system designed to allow users to route DICOM-compliant data to and from automated processing components.Supported modalities include CT, MR, RTSTRUCT.Workflow Box includes processing components for automatically contouring imaging data using deformable image registration to support atlas based contouring, re-contouring of the same patient and machine learning based contouring.Workflow Box is a data routing and image processing tool which automatically applies contours to

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

		Registration, fusion display, and review of medical images for diagnosis, treatment evaluation, and treatment planning. Evaluation of cardiac left ventricular function and perfusion, including left ventricular enddiastolic volume, end-systolic volume, and ejection fraction. Localization and definition of objects such as tumors and normal tissues in medical images. Creation, transformation, and modification of contours for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy transferring contours to	data which is sent to one or more of the included image processing workflows. Contours generated by Workflow Box may be used as an input to clinical workflows including, but not limited to radiation therapy treatment planning. Workflow Box must be used in conjunction with appropriate software to review and edit results generated automatically by Workflow Box components, for example image visualization software must be used to facilitate the review and edit of contours generated by Workflow Box component applications. Workflow Box is intended to be used by trained medical professionals. Workflow Box is not intended to automatically detect lesions.
		radiationtherapy
		transferring contours to
		radiation therapy treatment
		planning systems,and
		archiving contours for
		patientfollow-upand
		management.
	●	Quantitative and statistical
		analysis of PET/SPECT
		brain scans by comparing
		to other
		PET/SPECT brain scans.
		Planning and evaluation of
		permanentimplant
		brachytherapyprocedures
		(not including radioactive
		microspheres).
	●	Calculatingabsorbed
		radiation dose as a result of
		administeringa
		radionuclide. When using
		device clinically, the user
		should only use FDA
		approved
		radiopharmaceuticals. If
		using with unapproved

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

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

Label/labeling	Conform with 21CFRPart 801	Conform with 21CFR Part 801	Conform with 21CFR Part 801	Conform with 21CFR Part 801	Conform with 21CFR Part 801
OperatingSystem	Windows	Windows	Windows and MAC system	Windows and MAC system	Windows
Segmentation FeaturesAlgorithm	Deep Learning	Deep Learning	Atlas-based	Atlas-based	Atlas Based contouring
			ones, this device shouldonly be used for researchpurposes.Lossy compressedmammographic images anddigitized film screen imagesmust not be reviewed forprimary image interpretations.Images that are printed to filmmust be printed using anFDA-approved printer for thediagnosis of digitalmammography images.Mammographic images must beviewed on a display system thathas been cleared by the FDA forthe diagnosis of digitalmammography images. Thesoftware is not to be used formammography CAD.
					registration based
					re-contouring, machine learningbased contouring
CompatibleModality	Non-Contrast CT	Non-Contrast CT	Non-Contrast CT	Non-Contrast CT	CT、MR
CompatibleScanner Models	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.
CompatibleTreatmentPlanningSystem	No Limitation on TPS model, DICOM3.0 compliance required.	No Limitation on TPS model, DICOM3.0 compliance required.	No Limitation on TPS model, DICOM3.0 compliance required.	No Limitation on TPS model, DICOM3.0 compliance required.	No Limitation on TPS model, DICOM3.0 compliance required.
Unattendedworkstation	Yes	No	Not stated	No	Yes
Registration Features
Algorithm	Intensity Based.	Intensity Based.	Intensity Based.	Intensity Based.	Intensity Based.
Imageregistration	Auto rigid registration and auto deformable registration.	Auto rigid registration	Auto rigid registration and deformable registration.	Auto rigid registration and deformable registration.	Auto rigid registration and deformable registration.
CompatibleModality	Auto rigid registration:CT, MRI, PETAuto deformableregistration: CT, MRI, CBCT	CT, MRI, PET	CT, MRI, CR, DX, MG, US, SPECT, PET and XA	PET/SPECT/CT/MRI	CT, MRI
	CompatibleScanner Models
CompatibleScanner Models	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.
CompatibleTreatmentPlanningSystem	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM 3.0 compliance required.
Plan Evaluation Feature
Display ofDICOM RTPlans	Yes	No	Not stated	Yes	No
Isodose LineDisplay	Yes	No	Not stated	Yes	No
DVH statisticsdisplay	Yes	No	Not stated	Yes	No
RT Planscomparison	Yes	No	Not stated	Yes	No
Dose Analysis Feature
Display ofDICOM RTDoses	Yes	No	Not stated	Yes	No
Doseaccumulation	Yes	No	Not stated	Yes	No

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

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

{11}------------------------------------------------

VII. PERFORMANCE DATA

The following performance data were provided in support of the substantial equivalence determination.

1. Non-Clinical Test Conclusion

Deformable registration performance test

The registration performance test was performed on proposed device and reference device (K182624) to evaluate the deformable registration accuracy. All fixed images and moving images are generated in healthcare institutions in U.S. The scanner models covered products from five major vendors. The image registration feature is tested on multi-modality image sets from different patients. The Normalized Mutual Information (NMI) was used for evaluation. NMI values were calculated on two sets of images for both the proposed device and reference device (K182624), respectively. The NMI value of proposed device was compared with that of the reference device. According to the results, it could be concluded that the NMI of proposed device was non-inferior to that of the reference device.

2. Clinical Test Conclusion

No clinical study is included in this submission.

3. Software Verification and Validation Testing

Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device was considered as a "major" level of concern.

Software bench testing in the form of Unit, System and Integration tests were performed to evaluate the performance and functionality of the new features and software updates. Software verification and regression testing have been performed successfully to meet their previously determined acceptance criteria as stated in the test plans.

VIII. SUBSTANIALLY EQUIVALENT (SE) CONCLUSION

The proposed device is substantially equivalent to the predicate device AccuContour™ (K191928).

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).