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.
Device Story
Post-processing software plugin for GE Advantage Workstation/Server; processes 3D T1-weighted and T2-weighted FLAIR MR images. Uses atlas-based segmentation to identify brain structures and classify WMH candidates. Outputs include segmentations, visualizations, and volumetric measurements (absolute/relative). Used by radiologists/trained medical professionals for clinical assessment and longitudinal comparison of patient exams. User must review and edit WMH candidates before final validation. Provides quantitative data to support clinical decision-making and reporting.
Clinical Evidence
Bench testing only. Performance validated against manual segmentations on 33 3DT1w MR images for brain tissue (Dice: CSF 0.78, GM 0.84, WM 0.86, ICV 0.97) and 45 images for WMH (Dice 0.61 ± 0.13). Absolute volume differences reported for tissue and WMH. No clinical prospective/retrospective studies.
Technological Characteristics
Post-processing software plugin; atlas-based segmentation; compatible with GE AW 4.7/AWS 3.2. Inputs: 3D T1-weighted and T2-weighted FLAIR MR images. Compliant with ISO 14971 and IEC 62304. Algorithm uses atlas-based tissue segmentation and feature-based classification for WMH.
Indications for Use
Indicated for trained medical professionals to perform automatic labeling, visualization, and volumetric quantification of brain structures (GM, WM, CSF) and WMH candidates from MR images to aid in quantitative reporting.
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 two logos. The logo on the left is the Department of Health & Human Services - USA logo. The logo on the right is the FDA U.S. Food & Drug Administration logo. The FDA logo is in blue.
Quantib B.V. % Floor van Leeuwen Quality & Regulatory Manager Westblaak 106 3012 KM Rotterdam NETHERLANDS
March 9, 2018
Re: K173939
Trade/Device Name: QuantibTM Brain 1.3 Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: December 22, 2017 Received: December 26, 2017
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. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820);
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# Page 2 - Floor Leeuwen
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 the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.
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/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 For
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) K173939
Device Name QuantibTM Brain 1.3
# Indications for Use (Describe)
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.
| Type of Use (Select one or both, as applicable) | |
|--------------------------------------------------------------------------------------|----------------------------------------------------------------------|
| <input checked="true" type="checkbox"/> Prescription Use (Part 21 CFR 801 Subpart D) | <input type="checkbox"/> Over-The-Counter Use (21 CFR 801 Subpart C) |
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# Quantib™ Brain 1.3 510(k) Summary
Image /page/3/Picture/1 description: The image shows the logo for Quantib. The logo consists of a blue and light blue icon on the left and the word "Quantib" in dark blue on the right. The icon is made up of circles connected by lines, forming a network-like structure.
#### SUBMITTER 1
Quantib B.V. Westblaak 106 3012 KM Rotterdam Phone: (+31) 108 41 17 49 Contact Person: Floor van Leeuwen Date Prepared: December 20, 2017
#### Device 2
Name of Device: Quantib™ Brain 1.3 Common or Usual Name: Quantib™ Brain Classification Name: System, image processing, radiology (892.2050) Regulatory Class: II Product Code: Picture archiving and communication system (LLZ)
## Predicate device 3
Device: Quantib™ Brain 1.2 Manufacturer: Quantib BV 510(k) Reg. No: K163013 This predicate has not been subject to a design-related recall Requlatory Class: II Product Code: Picture archiving and communication system (LLZ)
#### Device Description 4
Quantib™ Brain is post-processing analysis software for the GE Advantage Workstation (AW 4.7) and AW Server (AWS 3.2) platforms using Volume Viewer Apps. 13.0 Ext 4 (or higher). 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. Quantib™ Brain provides quantitative information on both the absolute and relative volume of the segmented 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.
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Longitudinal analysis can be performed for the brain tissue segmentation and WMH seqmentation in order to compare multiple exams of an individual patient. Quantib Brain 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™ Brain 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 with predicate device
The intended use of the modified device is equal to the intended use of the previously cleared predicate device [K163013].
## 6 DEVICE MODIFICATIONS
Quantib™ Brain 1.3 is an update of Quantib™ Brain 1.2 (the predicate device). The differences are the following:
#### 6.1 BRAIN VOLUMETRY ALGORITHM
To improve the ICV results, a different voting mechanism is applied.
#### 6.2 WHITE MATTER HYPERINTENSITIES ALGORITHM
In the White Matter Hyperintensities segmentation algorithm of Quantib Brain 1.3 we have implemented a different type of classify the set of candidate lesions. To improve the algorithm performance, we have extended the set of features used in the classifier.
#### 6.3 LONGITUDINAL REVIEW ALGORITHM
No changes are made to the longitudinal review algorithm.
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## 7 Technological Characteristics
The following technological characteristics are the same for Quantib™ Brain 1.3 and its predicate device Quantib Brain 1.2:
- . Intended use and Indications for use
- . Target users, anatomical site and usage location
- Design .
- Compatibility with the environment and other devices .
The following technological characteristics are different:
- . Performance: Assessment of the performance of the new WMH algorithm is added. The dataset used for validation has been extended with additional patient data.
- . Reported measures: When a contrast-enhanced 3DT1w scan (in combination with a T2w FLAIR scan) are used as input data, the absolute and relative cross sectional volumes of the brain tissues, and therefore also the relative WMH volumes, are not reported. Absolute WMH measures and WMH longitudinal analysis are displayed.
- . Required input: Contrast-enhanced 3DT1w scans are no longer excluded from processing of WMH. However, not all measures are reported to the user whenever a contrast-enhanced scan is used as input
#### 8 PERFORMANCE DATA:
# 1. Quality and safety
Quantib™ Brain 1.3 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
The following quality assurance measures were applied to Quantib™ Brain 1.3 development:
- Risk and hazard analysis ●
- Design reviews
- Unit level testing ●
- Integration testing ●
- System testing
- Performance testing ●
- . Usability engineering
# 2. Algorithm performance
To validate the quality of Quantib™ Brain volume measurements and segmentations, the relative volumes and the segmentations were compared to relative volumes derived from manual segmentations and to manual segmentations of the same scan. This analysis was performed for GM, WM, CSF, ICV, and WMHs.
For Brain Volumetry (segmentation and measures of GM, WM, CSF, and ICV) the test set included 33 3DT1w MR images. 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. The results are summarized below.
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| | Dice index | Absolute difference of the relative<br>volumes [pp] |
|-----|-------------|-----------------------------------------------------|
| CSF | 0.78 ± 0.05 | 1.8 ± 1.0 |
| GM | 0.84 ± 0.02 | 2.7 ± 2.0 |
| WM | 0.86 ± 0.02 | 2.8 ± 1.9 |
| ICV | 0.97 ± 0.00 | |
Results of comparison between manual and automatic brain tissue segmentation. Reported values are averages ± std. dev., computed over 6 segmented slices of 33 scans. The Dice index provides a measure for overlap of manual and automatic segmentations (1 = perfect overlap). The absolute differences of the relative volumes (of the brain tissues) are averages ± std. dev. in percentage points.
The test set for the White Matter Hyperintensities protocol included 45 3DT1w 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™ Brain automatic segmentation output. The average Dice overlap between the manual segmentations and Quantib™ Brain segmentations was 0.61 ± 0.13 (over all cases). The absolute difference of the relative volumes (for WMHs) was 0.5 ± 0.5 percentage points (over 38 cases without contrast-enhancement).
## CONCLUSIONS ഗ
By virtue of its intended use and physical and technological characteristics, Quantib™ Brain 1.3 is substantially equivalent to a device that has been approved for marketing in the United States. The performance data shows that Quantib™ Brain 1.3 is as safe and effective as the predicate device.
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Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
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.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
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).
2. The results table
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.
Where do you find a device's intended use without opening the PDF?
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.
What does the AI Performance sub-table show, and why is it useful?
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.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
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.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
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.
8. Chart view
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.
10. Embeddings: the galaxy map
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.