Atlas-based segmentation with histogram analysis and trained classifiers
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Dice index: 0.61 ± 0.13
30 3D T1w images with corresponding T2w FLAIR images
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Indications for Use
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
Quantib™ Brain is a post-processing software plugin for GE Advantage Workstation (AW 4.7) or AW Server (AWS 3.2) platforms. It processes 3D T1-weighted MR images and T2-weighted FLAIR MR images. The device uses atlas-based segmentation, histogram analysis, and trained classifiers to automatically label and quantify brain structures (GM, WM, CSF) and WMH candidates. The system outputs segmentations, visualizations, and volumetric measurements (absolute and relative to intracranial volume). Radiologists or trained medical professionals review and manually edit WMH candidates via provided tools before final validation. The output provides objective, quantitative data to support diagnostic decision-making and follow-up assessments, potentially improving the efficiency and reproducibility of brain structure quantification compared to manual methods.
Clinical Evidence
Bench testing only. Validation compared automatic segmentations to manual segmentations on 33 T1w MR images for brain tissue (GM, WM, CSF, ICV) and 30 T1w/T2w FLAIR image pairs for WMH. For brain tissue, Dice indices were 0.78 (CSF), 0.83 (GM), 0.86 (WM), and 0.97 (ICV). Absolute differences in relative volumes were 1.6 pp (CSF), 2.8 pp (GM), and 2.6 pp (WM). For WMH, average Dice overlap was 0.61 ± 0.13 with an absolute difference in relative volume of 0.6 ± 0.7 percentage points.
Technological Characteristics
Software-based image processing; atlas-based segmentation principle; utilizes histogram analysis and trained classifiers. Inputs: 3D T1-weighted MR and T2-weighted FLAIR images. Outputs: volumetric measurements and segmentations. Platform: GE Advantage Workstation (AW 4.7) or AW Server (AWS 3.2). Developed per ISO 14971 and IEC 62304 standards.
Indications for Use
Indicated for trained medical professionals to assist in the evaluation, assessment, and quantitative reporting of MR brain images. Used for automatic labeling, visualization, and volumetric quantification of brain structures (GM, WM, CSF) and white matter hyperintensity (WMH) candidates.
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/1 description: The image is a black and white logo for the U.S. Department of Health & Human Services. The logo features the department's name in a circular arrangement around a symbol. The symbol is a stylized representation of three human profiles facing right, with flowing lines suggesting movement or connection.
Food and Drug Administration 10903 New Hampshire Avenue Document Control Center - WO66-G609 Silver Spring, MD 20993-0002
June 17, 2016
Quantib B.V. Floor van Leeuwen Quality & Regulatory Manager Westblaak 106 3012 KM Rotterdam NETHERLANDS
Re: K153351 Trade/Device Name: QuantibTM Brain 1 Regulation Number: 21 CFR 892.2050 Regulation Name: Picture Archiving and Communications System Regulatory Class: Class II Product Code: LLZ Dated: May 19, 2016 Received: May 23, 2016
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 (reporting of medical devicerelated adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in
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the quality systems (OS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
If you desire specific advice for your device on our labeling regulation (21 CFR Part 801), please contact the Division of Industry and Consumer Education at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm. 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.
You may obtain other general information on your responsibilities under the Act from the Division of Industry and Consumer Education at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm.
Sincerely yours,
Michael D'Hara
For
Robert 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) K-153351
Device Name Quantib™ Brain 1
#### 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)
| <span style="font-size: 10pt"> <span style="font-family: Arial"> <span style="font-size: 10pt"> <span style="font-family: Arial">☑ Prescription Use (Part 21 CFR 801 Subpart D)</span> </span> </span> </span> |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <span style="font-size: 10pt"> <span style="font-family: Arial"> <span style="font-size: 10pt"> <span style="font-family: Arial">☐ Over-The-Counter Use (21 CFR 801 Subpart C)</span> </span> </span> </span> |
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Image /page/3/Figure/0 description: The image shows the logo for Quantib, a company focused on image-informed medicine. The logo features a stylized graphic to the left, composed of stacked, offset rectangles in shades of blue, orange, and brown, creating a visual effect reminiscent of data or imaging slices. To the right of the graphic is the company name "Quantib" in a teal sans-serif font, with the tagline "Image Informed Medicine" in a smaller font size underneath.
# 510(k) Summary
## I. SUBMITTER
Quantib B.V. Westblaak 106 3012 KM Rotterdam Phone: (+31) 108 41 17 49 Contact Person: Floor van Leeuwen Date Prepared: November 18th, 2015
# II. DEVICE
Name of Device: Quantib™ Brain 1 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)
# III. PREDICATE DEVICE
Device: QBrain® Manufacturer: Medis medical imaging systems 510(k) Reg. No: K050703 This predicate has not been subject to a design-related recall. Regulatory Class: II Product Code: Picture archiving and communication system (LLZ)
# IV. DEVICE DESCRIPTION
Quantib™ Brain is post-processing analysis software for the GE Advantage (AW 4.7) or AW Server (AWS 3.2) platforms using Volume Viewer Apps. 12.3 Ext 6. It is intended for automatic labeling, visualization, and volumetric quantification of identifiable brain structures from magnetic resonance images (a 3D T1weighted 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. 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 radiology specialist in quantitative reporting.
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## V. INTENDED USE
#### Intended use Quantib™ Brain
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.
#### Intended us predicate device QBrain®
#### Intended use
The QBrain® software has been developed for the objective and reproducible analysis of MR images of the brain. It performs quantitative analyses on MR brain image based on automatic segmentation. More specifically, it quantifies the volumes of intracranial cavities, areas that contain cerebrospinal fluid (CSF), and white matter hyperintensities (lesions).
#### Indications for use
QBrain® is able to read DCIOM MR image from all major MRI vendors. Mask data, generated by automatic segmentation and/or manual editing, and quantitative results ca be saved in separate files enabling the comparison of results from different users and easy export to standard spreadsheet software.
Neur-(radio)logists in hospitals and specialists in core labs use the QBrain stand-alone analytical software package in image post-processing. The provided objective and quantitative values support the diagnostic decision process or are used in the evaluation of follow-up studies about on and/or therapy response.
#### Intended use comparison
Both device and predicate device are designed to assist trained medical professionals, i.e. radiologists, in the evaluation and assessment of MR brain images with a set of tools for visualizing the location and quantification of brain structures. Both are intended to automate the current manual process of identifying, labeling, and quantifying the volume of segmentable brain structures identified on MR images.
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# VI. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS
Both device and predicate device are software for the automatic labeling, visualization, and quantification (segmentation) of the volume of specific areas of the brain. Atlas-based segmentation is the technological principle for both devices.
Both devices require a 3D T1w MR image for tissue-type segment White Matter Hyperintensities (WMH), Quantib™ Brain requires an additional T2w FLAIR scan. Histogram analysis and trained classifiers are used in combination with atlas-based tissue segmentation to improve the accuracy and specificity of WMH segmentation.
Quantib™ Brain reports the following measurements: absolute volume and relative to ICV) of grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV); brain volume (=GM+WM); total volume of WM hyperintensities (WMH); relative WMH volume (relative to WM); total number of WMH.
| Quantib™ Brain | K050703 QBrain® |
|-------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|
| Brain tissue and WMH segmentation | Brain tissue segmentation (intracranial<br>tissue volume GM+WM, CSF volume, lobe<br>volumes, cerebellum volume). WMH<br>segmentation (total WMH volume). |
| Absolute and relative volumes | Absolute volumes |
| Software plugs into the AW and AW server<br>platforms | Stand-alone application |
Following are the differences between Quantib™ Brain and the predicate device:
Table 5-1 Differences with predicate device
# VII. PERFORMANCE DATA:
## 1. Quality and safety
Quantib™ Brain 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 development:
- Risk and hazard analysis
- Design reviews
- Unit level testing
- . Integration testing
- System testing
- Performance testing
- . Usability engineering
#### 2. Algorithm performance
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To validate the quality of Quantib™ Brain volume measurements and segmentations, we compared the relative brain tissue volumes to relative volumes derived from manual segmentations for the same scan. We performed this analysis for GM, WM, CSF, ICV, and WMHs.
For the brain volumetry protocol (segmentation and measures of GM, WM, CSF, and ICV) the test set included 33 T1w MR images. The set was carefully selected to include data from multiple vendors and a series of representable scan settings. For each scan we selected six (6) slices for comparison. The results are summarized in Table 5-2.
| | Dice index | Absolute difference of the<br>relative volumes [pp] |
|-----|-------------|-----------------------------------------------------|
| CSF | 0.78 ± 0.05 | 1.6 ± 1.0 |
| GM | 0.83 ± 0.02 | 2.8 ± 1.9 |
| WM | 0.86 ± 0.02 | 2.6 ± 1.6 |
| ICV | 0.97 ± 0.01 | |
Table 5-2 Results of comparison between manual and automatic brain tissue seqmentation. 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 WMH protocol included 30 3D T1w images with corresponding T2w FLAIR images. This set also represented various scan settings. WMHs were manually segmented on the T2w FLAR 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. The absolute difference of the relative volumes (for WMHs) was 0.6 ± 0.7 percentage points.
# VIII. CONCLUSIONS
By virtue of its intended use and physical and technological characteristics, Quantib™ Brain is substantially equivalent to a device that has been approved for marketing in the United States. The performance data shows that Quantib™ Brain 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.
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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.
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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.
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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.