(206 days)
Not Found
Yes
The device description explicitly states that Acorn Segmentation contains "machine learning based auto-segmentation" and "machine-learning-based fully automatic algorithms".
No.
The device is intended for image segmentation, measuring, and treatment planning, and for the fabrication of physical replicas for diagnostic purposes. It does not directly provide therapy or treatment.
No
The primary device, "Acorn Segmentation," is a software interface and image segmentation system intended for tasks like measuring and treatment planning. While it produces an output file that can be used to fabricate physical replicas ("Acorn 3DP Models"), it is these physical replicas, not the software itself, that are explicitly stated as being usable for "diagnostic purposes." The software's role is to facilitate the creation of these models for diagnostic use, not to be a diagnostic tool in itself.
No
While the core of the device is software for image processing and segmentation, the intended use and device description explicitly include the fabrication and use of a physical replica (Acorn 3DP Model) for diagnostic purposes. This physical replica is a hardware component that is part of the overall system described and intended for clinical use.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- Definition of IVD: An In Vitro Diagnostic device is a medical device intended for use in vitro for the examination of specimens, including blood, tissue, and urine, derived from the human body, solely or principally for the purpose of providing information concerning a physiological state, state of health, disease or congenital abnormality.
- Intended Use: The intended use of Acorn Segmentation and Acorn 3DP Models is focused on:
- Processing and segmenting medical images (CT/CTA).
- Measuring and treatment planning based on these images and resulting models.
- Fabricating physical replicas (3DP Models) from the digital models.
- Using the physical replica for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.
- Lack of Specimen Examination: The device does not involve the examination of biological specimens (blood, tissue, urine, etc.) derived from the human body. Its input is medical imaging data.
- Focus on Imaging and Physical Models: The core function is image processing, segmentation, and the creation of digital and physical models based on anatomical structures visualized in the images. While the physical replica can be used for diagnostic purposes, this is based on the morphology derived from the imaging data, not on the analysis of biological markers or components within a specimen.
Therefore, while the device is a medical device used for diagnostic purposes (specifically, the physical replica), it does not meet the criteria of an In Vitro Diagnostic device because it does not perform in vitro examination of biological specimens.
Yes
The letter explicitly states, "FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP)."
Intended Use / Indications for Use
Acorn Segmentation is intended for use as a software interface and image segmentation system for the transfer of CT or CTA medical images to an output file. Acorn Segmentation is also intended for measuring and treatment planning. The Acorn Segmentation output can also be used for the fabrication of the output file using additive manufacturing methods, Acorn 3DP Models. The physical replica can be used for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.
Acorn Segmentation and 3DP Models should be used in conjunction with expert clinical judgment.
Product codes
QIH, LLZ
Device Description
Acorn Segmentation is an image processing software that allows the user to import, visualize and segment medical images, check and correct the segmentations, and create digital 3D models. The models can be used in Acorn Segmentation for measuring, treatment planning and producing an output file to be used for additive manufacturing (3D printing). Acorn Segmentation is structured as a modular package. This includes the following functionality:
- Importing medical images in DICOM format
- Viewing images and DICOM data
- Selecting a region of interest using generic segmentation tools
- Segmenting specific anatomy using dedicated semi-automatic tools or automatic algorithms
- Verifying and editing a region of interest
- Calculating a digital 3D model and editing the model
- Measuring on images and 3D models
- Exporting 3D models to third-party packages
Acorn Segmentation contains both machine learning based auto-segmentation as well as semi-automatic and manual segmentation tools. The auto-segmentation tool is only intended to be used for thoracic and lumbar regions of the spine (T1-T12 and L1-L5). Semi-automatic and manual segmentation tools are intended to be used for all musculoskeletal and craniomaxillofacial anatomy. The following table provides a definition and the anatomical location(s) for each tool's intended use.
Acorn 3DP Model is an additively manufactured physical replica of the digital 3D model generated in Acorn Segmentation. The output file from Acorn Segmentation is used to additively manufacture the Acorn 3DP Model.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes - "Acorn Segmentation contains both machine learning based auto-segmentation as well as semi-automatic and manual segmentation tools"
Input Imaging Modality
CT or CTA medical images, DICOM format
Anatomical Site
Musculoskeletal and craniomaxillofacial applications. For auto-segmentation, specifically thoracic (T1-T12) and lumbar (L1-L5) regions of the spine.
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Used in conjunction with expert clinical judgment, implies use by medical professionals in a clinical setting.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The geometric accuracy of digital models created in the subject device, Acorn Segmentation, was assessed via bench testing of automatic, semi-automatic, and manual segmentation methods. Accuracy of automatic segmentation was evaluated for its intended use on Thoracic (T1-T12) and Lumbar (L1-L5) anatomy. Accuracy of semi-automatic and manual segmentation methods was evaluated for their intended for use on musculoskeletal and craniomaxillofacial anatomy. All segmentations were evaluated against predicate device segmentations using Dice-Sorenson coefficient.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Software verification and validation were performed and documentation was provided following the "Guidance for the Content of Premarket Submissions for Software in Medical Devices". This includes verification against defined requirements, and validation against user needs. Both end-user validation and bench testing were performed.
The geometric accuracy of digital models created in the subject device, Acorn Segmentation, was assessed via bench testing of automatic, semi-automatic, and manual segmentation methods. Accuracy of automatic segmentation was evaluated for its intended use on Thoracic (T1-T12) and Lumbar (L1-L5) anatomy. Accuracy of semi-automatic and manual segmentation methods was evaluated for their intended for use on musculoskeletal and craniomaxillofacial anatomy. All segmentations were evaluated against predicate device segmentations using Dice-Sorenson coefficient. Testing of automatic, semi-automatic, and manual segmentation methods each exceeded an average Dice-Sorenson coefficient of 0.93. All deviations were within the acceptance criteria. This shows that for creating digital models, Acorn Segmentation is substantially equivalent to the predicate device.
The geometric accuracy of physical replicas (produced by 3D printing digital models) was also assessed. This was conducted for various representative musculoskeletal and craniomaxillofacial models. Testing showed that the physical models can be printed accurately at less than 1mm mean deviation when compared to the digital models.
In conclusion, all performance testing conducted demonstrated device performance and substantial equivalence to the predicate device.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Testing of automatic, semi-automatic, and manual segmentation methods each exceeded an average Dice-Sorenson coefficient of 0.93.
The physical models can be printed accurately at less than 1mm mean deviation when compared to the digital models.
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
§ 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).
0
Image /page/0/Picture/0 description: The image shows 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 consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, with the word "ADMINISTRATION" underneath. The logo is simple and professional, and it is easily recognizable.
Mighty Oak Medical Mark Wylie VP Quality and Regulatory 750 W. Hampden Ave Suite 120 Englewood, Colorado 80110
July 12, 2024
Re: K234009
Trade/Device Name: Acorn 3D Software (AC-SEG-4009); Acorn 3DP Model (AC-101-XX) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH, LLZ Dated: June 12, 2024 Received: June 12, 2024
Dear Mark Wylie:
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 (the 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 available 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.
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an
1
established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (OS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-regulatory
2
assistance/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,
Jessica Lamb
Jessica Lamb Assistant Director DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
3
Indications for Use
510(k) Number (if known) K234009
Device Name Acorn 3D Software (AC-SEG-4009) Acorn 3DP Model (AC-101-XX)
Indications for Use (Describe)
Acorn Segmentation is intended for use as a software interface and image segmentation system for the transfer of CT or CTA medical images to an output file. Acorn Segmentation is also intended for measuring and treatment planning. The Acorn Segmentation output can also be used for the fabrication of the output file using additive manufacturing methods, Acorn 3DP Models. The physical replica can be used for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.
Acorn Segmentation and 3DP Models should be used in conjunction with expert clinical judgment.
| 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."
4
Image /page/4/Picture/2 description: The image shows the logo for Mighty Oak. The logo features three green curved lines above the words "MIGHTY OAK" in blue. The curved lines are arranged in a way that suggests movement or growth.
MEDICA
Acorn 3D Software (AC-SEG-4009) and Acorn 3DP Model (AC-101-XX)
Submitter:
Mighty Oak Medical 750 W. Hampden Ave., Suite 120 Englewood, CO 80110 (720) 398-9703
| Contact: | Mark A. Wylie, VP of Quality and Regulatory |
|---|---|
| Date Prepared: | 12JUL2024 |
Device
| Trade Name: | Acorn 3D Software (AC-SEG-4009); Acorn 3DP Model (AC-101-XX) |
|---|---|
| Common Name: | Image processing system |
| Device Classification: | Class II |
| Regulation, Name: | 21 CFR 892.2050, Medical image management and processing system |
| Device Product Code: | QIH, LLZ |
Type of 510(k)
Original Submission: Traditional
Predicate Device(s):
Acorn 3D Software (AC-SEG-4009); Acorn 3DP Model (AC-101-XX)
| 510(k) | Product Code | Trade Name | Manufacturer |
|---|---|---|---|
| Primary Predicate Device | |||
| K183105 | LLZ | Mimics Medical | Materialise NV |
| Subsequent Predicate Device | |||
| K183489 | LLZ | D2P | 3D Systems, Inc |
Description
Acorn Segmentation is an image processing software that allows the user to import, visualize and segment medical images, check and correct the segmentations, and create digital 3D models. The models can be used in Acorn Segmentation for measuring, treatment planning and producing an output file to be used for additive manufacturing (3D printing). Acorn Segmentation is structured as a modular package. This includes the following functionality:
- . Importing medical images in DICOM format
- · Viewing images and DICOM data
- · Selecting a region of interest using generic segmentation tools
- · Segmenting specific anatomy using dedicated semi-automatic tools or automatic algorithms
- · Verifying and editing a region of interest
- · Calculating a digital 3D model and editing the model
- · Measuring on images and 3D models
- · Exporting 3D models to third-party packages
5
Acorn Seamentation contains both machine learning based auto-seamentation as well as semiautomatic and manual segmentation tools. The auto-segmentation tool is only intended to be used for thoracic and lumbar regions of the spine (T1-T12 and L1-L5). Semi-automatic and manual seamentation tools are intended to be used for all musculoskeletal and craniomaxillofacial anatomy. The following table provides a definition and the anatomical location(s) for each tool's intended use.
| Automatic | Semi-Automatic | Manual | |
|---|---|---|---|
| Definition | Algorithmic with little or no | ||
| direct human control | A combination of | ||
| algorithmic and direct | |||
| human control | Directly controlled by a | ||
| human | |||
| Tool Type | Machine Learning | ||
| algorithm used to | |||
| automatically segment | |||
| individual vertebrae | Algorithmic based tools | ||
| that do not incorporate | |||
| machine learning. | Manual tools requiring user | ||
| input. | |||
| Anatomical | |||
| Location (s) | Spinal anatomy: | ||
| · Thoracic (T1-T12) | |||
| • Lumbar (L1-L5) | Musculoskeletal & | ||
| craniomaxillofacial bone: | |||
| · Short | |||
| · Long | |||
| · Flat | |||
| · Sesamoid | |||
| · Irregular | Musculoskeletal & | ||
| craniomaxillofacial bone: | |||
| · Short | |||
| · Long | |||
| · Flat | |||
| · Sesamoid | |||
| · Irregular |
Acorn 3DP Model is an additively manufactured physical replica of the digital 3D model generated in Acorn Segmentation. The output file from Acorn Segmentation is used to additively manufacture the Acorn 3DP Model.
Indications for Use
Acorn Seamentation is intended for use as a software interface and image segmentation system for the transfer of CT or CTA medical images to an output file. Acorn Segmentation is also intended for measuring and treatment planning. The Acorn Seamentation output can also be used for the fabrication of physical replicas of the output file using additive manufacturing methods, Acorn 3DP Models. The physical replica can be used for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.
Acorn Segmentation and 3DP Models should be used in conjunction with expert clinical judgment.
Materials
Acorn 3DP materials used have been tested and shown to be biocompatible in accordance with ISO 10993-1. The material used to manufacture Acorn 3DP Models is a PA-12 polymer powder for use in HP multi-jet fusion systems.
Performance Data
Software verification and validation were performed and documentation was provided following the "Guidance for the Content of Premarket Submissions for Software in Medical Devices". This includes verification against defined requirements, and validation against user needs. Both end-user validation and bench testing were performed.
The geometric accuracy of digital models created in the subject device, Acorn Segmentation, was assessed via bench testing of automatic, semi-automatic, and manual segmentation methods. Accuracy of automatic segmentation was evaluated for its intended use on Thoracic (T1-T12) and Lumbar (L1-L5) anatomy. Accuracy of semi-automatic and manual segmentation methods was evaluated for their intended for use on musculoskeletal and craniomaxillofacial anatomy. All segmentations were evaluated against predicate device segmentations using Dice-Sorenson coefficient. Testing of automatic, semi-automatic, and manual segmentation methods each exceeded an average Dice-Sorenson coefficient of 0.93. All deviations were within the acceptance criteria. This shows that for creating digital models, Acorn Segmentation is substantially equivalent to the predicate device.
6
The geometric accuracy of physical replicas (produced by 3D printing digital models) was also assessed. This was conducted for various representative musculoskeletal and craniomaxillofacial models. Testing showed that the physical models can be printed accurately at less than 1mm mean deviation when compared to the digital models.
In conclusion, all performance testing conducted demonstrated device performance and substantial equivalence to the predicate device.
Technological Characteristics
The following technological characteristics of the subject Acorn Seamentation & 3DP Model are equivalent to the predicate devices. These include:
- . Importing medical images in DICOM format
- . Viewing images and DICOM data
- Selecting a region of interest using generic segmentation tools ●
- Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic ● algorithms
- . Verifying and editing a region of interest
- . Calculating a digital 3D model and editing the model
- Measuring on images and 3D models ●
- . Exporting 3D models to third-party packages
Technological characteristics which are different have been supported with descriptive information and/or performance data. Therefore the fundamental scientific technology of Acorn Segmentation & 3DP Model is the same as the previously cleared device.
Substantial Equivalence Comparison Table
The table below provides a descriptive comparison of the similarities and differences for the subject and predicate devices. The items marked bold in the table highlight the differences between the Acorn Segmentation & 3DP Model and the predicate (Materialise's Mimics Medical, K183105).
| Device→ | Acorn Segmentation
(K234009) | Mimics Medical
(K183105) | D2P
(K183489) |
|----------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Features↓ | | | |
| Premarket
notification | K234009 | K183105 | K183489 |
| Manufacturer | Mighty Oak Medical | Materialise N.V. | 3D Systems |
| Indications for Use
Statement | Acorn Segmentation is intended for use as a
software interface and image segmentation
system for the transfer of CT or CTA medical
imaging information to an output file. Acorn
Segmentation is also intended for measuring and
treatment planning. The Acorn Segmentation
output can also be used for the fabrication of
physical replicas of the output file using additive
manufacturing methods, Acorn 3DP Models. The
physical replica can be used for diagnostic
purposes in the field of musculoskeletal and
craniomaxillofacial applications.
Acorn Segmentation and 3DP Models should be
used in conjunction with expert clinical judgment. | Mimics is intended for
use as a software
interface and image
segmentation system
for the transfer of
medical imaging
information to an
output file. Mimics
Medical is also
intended for
measuring and
treatment planning.
The Mimics Medical
output can be used
for the fabrication of
physical replicas of
the output file using
traditional or additive
manufacturing
methods.
The physical replica
can be used for
diagnostic purposes in | The D2P software is
intended for use as a
software interface
and image
segmentation system
for the transfer of
DICOM imaging
information from a
medical scanner to
an output file. It is also
intended as pre-
operative software for
surgical planning. For
this purpose, the
output file may be
used to produce a
physical replica. The
physical replica is
intended for
adjunctive use along
with other diagnostic
tools and expert
clinical judgement for |
| Device→
Features↓ | Acorn Segmentation
(K234009) | Mimics Medical
(K183105) | D2P
(K183489) |
| | | the field of
orthopaedic,
maxillofacial and
cardiovascular
applications.
Mimics Medical
should be used in
conjunction with
expert clinical
judgment. | diagnosis, patient
management, and/or
treatment selection of
cardiovascular,
craniofacial,
gastrointestinal,
genitourinary,
neurological, and/or
musculoskeletal
applications. |
| General intended
use | Acorn Segmentation is image processing software
that allows the user to import, visualize and
segment medical images, check and correct the
segmentations, and create digital 3D models. | Mimics Medical is
image processing
software that allows
the user to import,
visualize and segment
medical images,
check and correct the
segmentations, and
create digital 3D
models. | D2P is image
processing software
that allows the user to
import, visualize and
segment medical
images, check and
correct the
segmentations, and
create digital 3D
models. |
| Product
Classification | System, Image processing, Radiological | System, Image
processing,
Radiological | System, Image
processing,
Radiological |
| Regulatory Class | Class II | Class II | Class II |
| Regulation
Number | 892.2050 | 892.2050 | 892.2050 |
| Product Code | QIH, LLZ | LLZ | LLZ |
| Device
Description | Acorn Segmentation is an image processing
software that allows the user to import, visualize
and segment medical images, check and
correct the segmentations, and create digital 3D
models. The models can be used in Acorn
Segmentation for measuring, treatment planning
and producing an output file to be used for
additive manufacturing (3D printing). Acorn
Segmentation is structured as a modular
package. This includes the following functionality:
Importing medical images in DICOM format Viewing images and DICOM data Selecting a region of interest using generic
segmentation tools Segmenting specific anatomy using
dedicated semi-automatic tools or fully
automatic algorithms Verifying and editing a region of interest Calculating a digital 3D model and editing
the model Measuring on images and 3D models Exporting 3D models to third-party packages Acorn Segmentation contains both machine
learning based auto-segmentation as well as
semi-automatic and manual segmentation tools.
The auto-segmentation tool is only intended to be
used for thoracic and lumbar regions of the spine
(T1-T12 and L1-L5). Semi-automatic and manual
segmentation tools are intended to be used for all
musculoskeletal and craniomaxillofacial
anatomy. The following table provides a definition | Mimics Medical is
image processing
software that allows
the user to import,
visualize and segment
medical images,
check and correct the
segmentations, and
create digital 3D
models. The models
can be used in Mimics
Medical for
measuring, treatment
planning and
producing an output
file to be used for
additive
manufacturing (3D
printing). Mimics
Medical also has
functionality for linking
to third party software
packages. Mimics
Medical is structured
as a modular
package. This includes
the following
functionality:
Importing
medical images
in DICOM format
and other
formats (such as | The D2P software is a
stand-alone modular
software package
that provides
advanced
visualization of DICOM
imaging data. This
modular package
includes, but is not
limited to the following
functions:
DICOM viewer
and analysis Automated
segmentation Editing and pre-
printing Seamless
integration with
3D Systems
printers Seamless
integration with
3D Systems
software
packages Seamless
integration with
Virtual Reality
visualization for
non-diagnostic
use. |
7
8
| Device→
Features↓ | Acorn Segmentation
(K234009)
and the anatomical location(s) for each tool's
intended use. | | | Mimics Medical
(K183105) | D2P
(K183489) |
|----------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | Automatic | Semi-
Automatic | Manual | BMP, TIFF, JPG
and raw images) Viewing images
and DICOM data Selecting a
region of interest
using generic
segmentation
tools Segmenting
specific anatomy
using dedicated
semi-automatic
tools or fully
automatic
algorithms Verifying and
editing a region
of interest Calculating a
digital 3D model
and editing the
model Measuring on
images and 3D
models Exporting
images,
measurements
and 3D models
to third-party
packages Planning
treatments
(surgical cuts
etc.) on the 3D
models Interfacing with
packages for
Finite Element
Analysis Creating Python
scripts to
automate
workflows | |
| | Definition | Algorithmic
with little or
no direct
human
control | A
combination
of
algorithmic
and direct
human
control | Directly
controlled
by a
human | |
| | Tool
Type | Machine
Learning
algorithm
used to
automat-
ically
segment
individual
vertebrae | Algorithmic
based tools
that do not
incorporate
machine
learning. | Manual
tools
requiring
user input. | |
| | Anatom-
ical
Location
(s) | Spinal
anatomy:
• Thoracic
(T1-T12)
• Lumbar
(L1-L5) | Musculo-
skeletal &
cranioma-
xillofacial
bone:
• Short
• Long
• Flat
• Sesamoid
• Irregular | Musculo-
skeletal &
craniuma-
xillofacial
bone:
• Short
• Long
• Flat
• Sesa-
moid
• Irreg-
ular | |
| Technological
characteristics | Acorn Segmentation is a standalone modular
software package. This module includes, but is
not limited to the following functions:
Image Import
• Importing medical images in DICOM format
Image Processing
• Processing of images with common noise-
reduction filters
• Editing of spatial arrangement of images
Visualization
• Viewing images and DICOM data
Segmentation
• Selecting a region of interest using generic
segmentation tools
• Segmenting specific anatomy using | | | Mimics Medical is
structured as a
modular package.
This includes the
following functionality:
Image Import
• Importing
medical images
in DICOM format
and other formats
(such as BMP,
TIFF, JPG and raw
images)
Image Processing
• Processing of
images with
common noise- | D2P is structured as a
modular package.
This includes the
following functionality:
Image Import
• Importing
medical images
in DICOM format
and other
formats (such as
BMP, TIFF, JPG
and raw images)
Image Processing
• Processing of
images with
common noise-
reduction filters |
9
| Device→
Features↓ | Acorn Segmentation
(K234009) | Mimics Medical
(K183105) | D2P
(K183489) |
|----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | Segmenting specific vertebral anatomy using machine-learning-based fully automatic algorithms Verifying and editing a region of interest | Editing of spatial arrangement of images Processing of imaging data for removal of common artifacts (e.g. scatter) | Editing of spatial arrangement of images |
| Measurement | Measuring on images and 3D models | Visualization Viewing images and DICOM data | Visualization Viewing images and DICOM data |
| Image Export | Exporting images and 3D models to third-party packages | Segmentation Selecting a region of interest using generic segmentation tools Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic algorithms Verifying and editing a region of interest | Segmentation Selecting a region of interest using generic segmentation tools Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic deep learning tools Verifying and editing a region of interest |
| 3D Models | Calculating a digital 3D model and editing the model Smoothing a 3D model Importing 3D models | Measurement Measuring on images and 3D models | Measurement Measuring on images and 3D models |
| | | Image Export Exporting images, measurements and 3D models to third-party packages | Image Export Exporting images and 3D models to third-party packages |
| | | 3D Models Calculating a digital 3D model and editing the model Wrapping a 3D model Smoothing a 3D model Importing 3D models | 3D Models Calculating a digital 3D model and editing the model Smoothing a model Importing 3D models |
| Treatment Planning | Importing of third-party STLs to visualize planned interactions with anatomy as represented in DICOM images | Treatment Planning Importing of third-party STLs to visualize planned interactions with anatomy as represented in DICOM images | Treatment Planning Importing of third-party STLs to visualize planned interactions with anatomy as represented in DICOM images Planning treatments (surgical cuts etc.) on the 3D models |
| Other features | Using a collection of images and masks as a training dataset for machine-learning segmentation algorithm | | Other features |
| Device→
Features↓ | Acorn Segmentation
(K234009) | Mimics Medical
(K183105) | D2P
(K183489) |
| | | Other features Interfacing with
packages for
Finite Element
Analysis Creating Python
scripts to
automate
workflows | |
| Physical Model | The Acorn Segmentation output can be used for
the fabrication of physical replicas of the output
file using additive manufacturing methods. The
physical replica can be used for diagnostic
purposes in the field of musculoskeletal and
craniomaxillofacial applications. | The Mimics Medical
output can be used
for the fabrication of
physical replicas of
the output file using
traditional or additive
manufacturing
methods. The physical
replica can be used
for diagnostic
purposes in the field of
orthopedic,
maxillofacial and
cardiovascular
applications. | The D2P output file
may be used to
produce a physical
replica. The physical
replica is intended for
adjunctive use along
with other diagnostic
tools and expert
clinical judgement for
diagnosis, patient
management, and/or
treatment selection of
cardiovascular,
craniofacial,
gastrointestinal,
genitourinary,
neurological, and/or
musculoskeletal
applications. |
10
Conclusion
Minor differences in intended use and technological characteristics exist, but performance data demonstrates that Acorn Segmentation & 3DP Model is as safe and effective, and performs as well as the predicate device for its intended use.