Dice index 0.96 ± 0.01; Absolute difference of relative volumes 1.7 ± 1.3 pp
—
—
Dataset A: 33 T1w MR images
>1 (manual segmenters)
Cerebrospinal fluid segmentation
—
—
Dice index 0.78 ± 0.05; Absolute difference of relative volumes 1.8 ± 1.3 pp
—
—
Dataset A: 33 T1w MR images
>1 (manual segmenters)
Intracranial volume segmentation
—
—
Dice index 0.98 ± 0.01
—
—
Dataset A: 33 T1w MR images
>1 (manual segmenters)
Hippocampus segmentation
—
—
Dice index 0.84 ± 0.03; Absolute difference of relative volumes 0.03 ± 0.02 pp
—
—
Dataset B: 89 T1w MR images
>1 (manual segmenters)
Frontal lobe segmentation
—
—
Dice index 0.95 ± 0.01; Absolute difference of relative volumes 1.95 ± 0.90 pp
—
—
Dataset C: 13 T1w MR images
>1 (manual segmenters)
Occipital lobe segmentation
—
—
Dice index 0.88 ± 0.03; Absolute difference of relative volumes 0.87 ± 0.75 pp
—
—
Dataset C: 13 T1w MR images
>1 (manual segmenters)
Parietal lobe segmentation
—
—
Dice index 0.89 ± 0.03; Absolute difference of relative volumes 2.81 ± 1.13 pp
—
—
Dataset C: 13 T1w MR images
>1 (manual segmenters)
Temporal lobe segmentation
—
—
Dice index 0.91 ± 0.01; Absolute difference of relative volumes 1.33 ± 0.76 pp
—
—
Dataset C: 13 T1w MR images
>1 (manual segmenters)
Cerebellum segmentation
—
—
Dice index 0.98 ± 0.01; Absolute difference of relative volumes 0.47 ± 0.20 pp
—
—
Dataset C: 13 T1w MR images
>1 (manual segmenters)
White matter hyperintensities segmentation
—
—
Dice index 0.61 ± 0.13; Absolute difference of relative volumes 0.2 ± 0.2 pp
—
—
45 3D T1w images with corresponding T2w FLAIR images
>1 (manual segmenters)
Indications for Use
Quantib™ ND is a non-invasive medical imaging processing application that is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures from a set of magnetic resonance (MR) images. The Quantib™ ND output consists of segmentations, visualizations and volumetric measurements of brain structures and white matter hyperintensities. Volumetric measurements may be compared to reference centile data. It is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the trained medical professional in quantitative reporting. Quantib™ ND is a software application on top of Myrian®.
Device Story
Quantib™ ND is a post-processing software module for Myrian® platform; operates on standard Windows hardware. Inputs: 3D T1-weighted MR images; optional T2-weighted FLAIR MR images for white matter hyperintensity (WMH) segmentation. Operation: Uses atlas-based segmentation to label and quantify brain structures (hippocampus, lobes, cerebellum) and WMH. Baseline module analyzes single time-points; Follow-Up module performs longitudinal analysis of two time-points. Output: Segmentations, visualizations, and volumetric measurements; comparison of volumes to reference centile data. Used by radiologists and trained medical professionals in clinical settings to assist in quantitative reporting. Benefits: Provides objective, automated volumetric data to support clinical assessment of brain structures and longitudinal changes.
Clinical Evidence
Bench testing only. Validation compared automatic segmentations to manual ground truth across three datasets (A, B, C). Dataset A (33 scans): Brain tissue Dice 0.96, CSF Dice 0.78, ICV Dice 0.98. Dataset B (89 scans): Hippocampus Dice 0.84. Dataset C (13 scans): Lobe segmentations (Frontal, Occipital, Parietal, Temporal, Cerebellum) showed Dice indices ranging from 0.87 to 0.98. WMH analysis (45 scans) showed average Dice 0.61. No clinical prospective or retrospective studies were required.
Technological Characteristics
Software-based image processing application; runs on Windows OS as an add-on to Myrian®. Uses atlas-based segmentation algorithms. Complies with ISO 14971 (risk management), IEC 62304 (software lifecycle), and IEC 62366 (usability). Inputs: T1w and T2w FLAIR MR images. Outputs: 3D segmentations, volumetric measurements, and centile comparisons.
Indications for Use
Indicated for trained medical professionals to aid in the evaluation, assessment, and quantitative reporting of MR brain images. Used for automatic labeling, visualization, and volumetric quantification of brain structures and white matter hyperintensities in patients undergoing MR imaging.
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).
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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The FDA logo is composed of two parts: the Department of Health & Human Services logo on the left and the FDA acronym along with the full name of the agency on the right. The FDA acronym is in blue, followed by the words "U.S. Food & Drug Administration" in blue.
December 27, 2018
Quantib BV Floor Van Leeuwen Ouality & Regulatory Manager Westblaak 106 ROTTERDAM, NL 3012 KM ZUID-HOLLAND
Re: K182564
Trade/Device Name: Ouantib ND Regulation Number: 21 CFR 892.2050 Regulation Name: Picture Archiving And Communications System Regulatory Class: Class II Product Code: LLZ Dated: November 12, 2018 Received: November 14, 2018
Dear Floor Van Leeuwen:
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 requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part
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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/CombinationProducts/GuidanceRegulatoryInformation/ucm597488.htm); 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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/) and CDRH Learn
(http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Michael D. O'Hara
Robert A. Ochs. Ph.D. Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known) K182564
Device Name Quantib™ ND 1.5
## Indications for Use (Describe)
Quantib™ ND is a non-invasive medical imaging processing application that is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures from a set of magnetic resonance (MR) images. The Quantib™ ND output consists of segmentations, visualizations and volumetric measurements of brain structures and white matter hyperintensities. Volumetric measurements may be compared to reference centile data. It is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the trained medical professional in quantitative reporting. Quantib™ ND is a software application on top of Myrian®.
| Type of Use (Select one or both, as applicable) | |
|-------------------------------------------------|--|
|-------------------------------------------------|--|
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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# Quantib™ ND 1.5 510(k) Summary
Image /page/3/Picture/1 description: The image shows the logo for Quantib. The logo consists of a blue atom-like structure on the left and the word "Quantib" in blue on the right. Below the word "Quantib" is the text "K182564".
#### SUBMITTER 1
Quantib B.V. Westblaak 106 3012 KM Rotterdam Phone: (+31) 108 41 17 49 Contact Person: Floor van Leeuwen Date Prepared: September 17th, 2018
## Device 2
Name of Device: Quantib™ ND 1.5 Common or Usual Name: Quantib™ ND Classification Name: System, image processing, radiology (892.2050) Regulatory Class: II Product Code: Picture archiving and communication system (LLZ)
### 3 PREDICATE DEVICE
Device: Quantib™ Brain 1.3 Manufacturer: Quantib B.V. 510(k) Reg. No: K173939 This predicate has not been subject to a design-related recall. Classification Name: System, image processing, radiology (892.2050) Requlatory Class: II Product Code: Picture archiving and communication system (LLZ)
#### Device description বা
Quantib™ ND is a post-processing analysis module for Myrian®, which provides 3D image visualization tools that create and display user-defined views and streamlines interpretation and reporting. It is intended for automatic labeling, visualization, and volumetric quantification of identifiable brain structures from magnetic resonance images (a 3D T1-weighted MR image, with an additional T2-weighted FLAIR MR image for white matter hyperintensities (WMH) segmentation). The segmentation system relies on a number of atlases each consisting of a 3D T1-weighted MR image and a label map dividing the MR image into different tissue segments.
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Quantib™ ND provides quantitative information on both the absolute and relative volume of the seqmented regions. The automatic WMH segmentation is to be reviewed and if necessary. edited by the user before validation of the segmentation, after which volumetric information is accessible. Quantib ND consists of Quantib ND Baseline, which provides analysis of images of one time-point, and Quantib ND Follow-Up, which provides longitudinal analysis of images of two time-points. Quantib ND Follow-Up can only process images that have been processed by Quantib ND Baseline. Quantib ND is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the radiology specialist in quantitative reporting.
### 5 Indications for Use
## Indications for use Quantib™ ND 1.5
Quantib™ ND is a non-invasive medical imaging processing application that is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures from a set of magnetic resonance (MR) images. The Quantib™ ND output consists of segmentations, visualizations and volumetric measurements of brain structures and white matter hyperintensities. Volumetric measurements may be compared to reference centile data. It is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the trained medical professional in quantitative reporting. Quantib™ ND is a software application on top of Myrian®.
## Indications for use predicate device (Quantib™ ND 1.3)
Quantib™ Brain is a non-invasive medical imaging processing application that is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures from a set of magnetic resonance (MR) images. The Quantib™ Brain output consists of segmentations, visualizations and volumetric measurements of grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The output also visualizes and quantifies white matter hyperintensity (WMH) candidates. Users need to review and if necessary, edit WMH candidates using the provided tools, before validation of the WMHs. It is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the trained medical professional in quantitative reporting. Quantib™ Brain is a post-processing plugin for the GE Advantage Workstation (AW 4.7) or AW Server (AWS 3.2) platforms.
## Indications for use comparison
The intended use of the device is partially equivalent to the intended use of the previously cleared predicate device [K173939]
#### Comparison of technological characteristics 6
The following technological characteristics are the same for Quantib™ ND 1.5 and its predicate device Quantib™ Brain 1.3:
- . Target users, anatomical site, and usage location
- . Design
- Standards met ●
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- . Required input
The following technological characteristics are different:
- . Indications for use and Intended use: The device is a software application on top of Myrian®; Seqmentation and quantification of separate lobes and hippocampus, comparison of whole brain, lobes, and hippocampus volumes to reference centile curves of a group representing the general population, and improved longitudinal volume change calculation using automated image registration are added.
- . Human factors: Similar workflow, but implementation is slightly different based on existing workflow of underlying software packages
- . Algorithm design For hippocampus segmentation, a slightly different refinement step is used than for other brain structures. Brain structures follow-up analysis is done using technological characteristics also applied elsewhere in Quantib ND and the predicate device.
- . Performance: For measures reported by both devices, performance numbers show slight changes, attributable to the underlying software packages. Assessment of the performance of segmentation of separate lobes, hippocampus, and reference centile curves is added.
- . Compatibility with the environment and other devices: Quantib™ ND is an add-on for Myrian, which can be installed on reqular hardware using Windows as operating system.
- Reported measures: Whole brain measures (sum of Grey Matter [GM] and White Matter ● [WM]) are reported instead of GM and WM separately. To the reported measures the volumes of the following structures are added: Hippocampus, Frontal Lobe, Occipital Lobe, Parietal Lobe, Temporal Lobe, and Cerebellum. Reference centile curves are added, comparing these results to a group representing the general population.
#### Performance data 7
#### 7.1 QUALITY AND SAFETY
Quantib™ ND 1.5 was designed in compliance with the following process standards:
- . ISO 14971 – Medical devices - Application of risk management to medical devices
- IEC 62304 Medical device software Software life cycle processes .
- IEC 62366 Medical devices Application of usability engineering to medical devices .
The following quality assurance measures were applied to Quantib™ ND 1.5 development:
- Risk and hazard analysis
- Design reviews .
- Unit level testing ●
- Integration testing ●
- System testing .
- Performance testing .
- . Usability engineering
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## 7.2 ALGORITHM PERFORMANCE
#### 7.2.1 Brain Structures
To validate the quality of Quantib™ ND volume measurements and segmentations, these were compared to manual seqmentations of the same scan and their derived volumes. This analysis was performed for Brain Tissue, CSF, ICV, Hippocampus, Frontal Lobe, Occipital Lobe, Parietal Lobe, Temporal Lobe, and Cerebellum.
For brain tissue, CSF, and ICV, the test set included 33 T1w MR images (Dataset A). The set was carefully selected to include data from multiple vendors and a series of representative scan settings. For each scan we selected six (6) slices for comparison. For the hippocampus the test set included 89 T1w images (Dataset B) and for the lobes the test set included 13 T1w MR images (Dataset C). For test sets B and C all slices were segmented manually for the comparison. The results are summarized below.
| | Dataset | Dice index | Absolute difference of<br>the relative volumes [pp] |
|-------------------|---------|-------------|-----------------------------------------------------|
| Brain | A | 0.96 ± 0.01 | 1.7 ± 1.3 |
| CSF | A | 0.78 ± 0.05 | 1.8 ± 1.3 |
| ICV | A | 0.98 ± 0.01 | - |
| Hippocampus total | B | 0.84 ± 0.03 | 0.03 ± 0.02 |
| Hippocampus right | | 0.84 ± 0.03 | 0.01 ± 0.01 |
| Hippocampus left | | 0.84 ± 0.03 | 0.01 ± 0.01 |
Results of comparison between manual and automatic brain structure segmentation. Reported values are averages ± std. dev., computed over 6 segmented slices of 33 scans (Dataset B all slices were segmented. The Dice index provides a measure for overlap of manual and automatic segmentations (1 = perfect overlap). The absolute differences of the relative volumes are averages ± std. dev. in percentage points.
| | Dataset | Dice index | Absolute difference of<br>the relative volumes [pp] |
|----------------------|---------|-------------|-----------------------------------------------------|
| Frontal lobe total | C | 0.95 ± 0.01 | 1.95 ± 0.90 |
| Frontal lobe right | | 0.94 ± 0.02 | 1.02 ± 0.61 |
| Frontal lobe left | | 0.94 ± 0.01 | 0.93 ± 0.50 |
| Occipital lobe total | C | 0.88 ± 0.03 | 0.87 ± 0.75 |
| Occipital lobe right | | 0.88 ± 0.03 | 0.43 ± 0.36 |
| Occipital lobe left | | 0.87 ± 0.04 | 0.53 ± 0.53 |
| Parietal lobe total | C | 0.89 ± 0.03 | 2.81 ± 1.13 |
| Parietal lobe right | | 0.88 ± 0.04 | 1.45 ± 0.80 |
| Parietal lobe left | | 0.88 ± 0.02 | 1.36 ± 0.56 |
| Temporal lobe total | C | 0.91 ± 0.01 | 1.33 ± 0.76 |
| Temporal lobe right | | 0.91 ± 0.02 | 0.72 ± 0.46 |
| Temporal lobe left | | 0.91 ± 0.01 | 0.61 ± 0.39 |
| Cerebellum total | C | 0.98 ± 0.01 | 0.47 ± 0.20 |
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| Cerebellum right | 0.97 ± 0.00 | 0.31 ± 0.13 |
|------------------|-------------|-------------|
| Cerebellum left | 0.97 ± 0.01 | 0.17 ± 0.11 |
Results of comparison between manual and automatic brain structure segmentation of the lobes. Reported values are averages ± std. dev., computed over 13 scans of which all slices were segmented (Dataset C). The Dice index provides a measure for overlap of manual and automatic segmentations (1 = perfect overlap). The absolute differences of the relative volumes are averages ± std. dev. in percentage points.
## 7.2.2 White Matter Hyperintensities
The test set for the White Matter Hyperintensities analysis included 45 3D T1w images, of which 7 contrast-enhanced, all with corresponding T2w FLAIR images. This set also represented various scan settings. WMHs were manually segmented on the T2w FLAIR images and compared to Quantib™ ND automatic segmentation output. The average Dice overlap between the manual segmentations and Quantib™ ND segmentations was 0.61 ± 0.13 (over all cases). The absolute difference of the relative volumes (for WMHs) was 0.2 ± 0.2 percentage points (over 38 cases without contrast-enhancement).
#### CONCLUSIONS 8
By virtue of its intended use and physical and technological characteristics, Quantib™ ND 1.5 is substantially equivalent to a device that has been approved for marketing in the United States. The performance data shows that Quantib™ ND 1.5 is as safe and effective as the predicate device.
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1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
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You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
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Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
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Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
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Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
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Reading rule for every project: how many summaries do you read in full?
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Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
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Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
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Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
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The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.