Improving Medical Image Segmentation with AI and Fake Data
This patent describes a method to make AI better at finding specific things in medical images, like tumors, by using computer-generated fake images to train the AI and a special network design.
Original patent title: “Medical image segmentation method based on U-Net”
This patent describes a method to make AI better at finding specific things in medical images, like tumors, by using computer-generated fake images to train the AI and a special network design. Granted to Nanjing University of Posts and Telecommunications in 2023 with 9 claims, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
This patent details a method for more accurately segmenting, or outlining, specific areas in medical images using a type of artificial intelligence called a U-Net. First, it takes existing medical images and their manual outlines (called 'real segmentation maps') and feeds them into a Generative Adversarial Network (GAN). This GAN creates new, realistic 'composite images' that look like real medical images but are computer-generated, helping to expand the training data (claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1, step 3). This expanded dataset is then used to train an 'improved multi-feature fusion segmentation network,' which is a modified U-Net (claim 1, step 5). A key improvement is the addition of a 'Dilated Convolution Module' between the network's shallow and deep feature connections, which helps the network better combine fine details with broader context, making it more adaptable to different target sizes and improving accuracy (claim 1, step 5). Finally, this trained network can accurately segment new medical images, for example, precisely outlining a tumor in an MRI scan.
The gap
What does this patent NOT cover?
- Does not cover medical image segmentation methods that do not use a Generative Adversarial Network (GAN) for data enhancement.
- Does not cover U-Net based segmentation networks that lack a Dilated Convolution Module in their skip connections.
- Does not cover image segmentation tasks for non-medical images, such as segmenting objects in photographs or satellite imagery.
- Does not cover medical image segmentation techniques that do not rely on a U-Net or a multi-feature fusion segmentation network architecture.
- Does not cover methods that do not use a Dice loss function for training the segmentation network, as specified in claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 4.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The cleverness lies in two main parts: first, using a Generative Adversarial Network (GAN) to create more training data, which helps prevent the AI from 'overfitting' to a small dataset. Second, it integrates a 'Dilated Convolution Module' into the U-Net's skip connections, allowing the network to see objects at different scales more effectively, thus improving its ability to find and outline targets of varying sizes.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Automated tumor detection in MRI or CT scans
Precise outlining of organs for radiation therapy planning
Quantifying lesion volumes in neurological scans
Identifying specific tissue types in pathology slides
Segmenting blood vessels in angiography images
Why it matters
The bigger picture
Accurate medical image segmentation is crucial for diagnosing diseases, planning treatments, and monitoring patient progress. This method aims to improve the precision of these tasks by addressing common challenges in AI training, such as limited access to diverse medical datasets and the need for robust feature recognition. By generating synthetic data and enhancing the network's ability to fuse different levels of detail, it helps medical professionals get clearer and more reliable outlines of areas of interest, like organs or lesions.
Filed
January 5, 2022
Granted
February 14, 2023
Market context
Who's building on this
Companies in this space
Nanjing University of Posts and Telecommunications, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively researching and developing in this area. Beyond academic institutions, major technology companies like Google Health and NVIDIA are investing heavily in AI for medical imaging. Numerous startups also focus on applying deep learning, including U-Net architectures and GANs, to enhance diagnostic capabilities in various medical specialties.
Market impact
This type of technology significantly impacts the medical diagnostics and research markets by enabling more accurate and automated analysis of medical images. It can lead to earlier disease detection, more precise treatment planning, and reduced workload for radiologists and pathologists. The ability to generate synthetic data helps overcome the challenge of limited real medical data, accelerating the development and deployment of AI solutions in healthcare, potentially making advanced diagnostics more accessible.
Claim 1 — Plain English
What this patent covers
This patent details a method for more accurately segmenting, or outlining, specific areas in medical images using a type of artificial intelligence called a U-Net. First, it takes existing medical images and their manual outlines (called 'real segmentation maps') and feeds them into a Generative Adversarial Network (GAN). This GAN creates new, realistic 'composite images' that look like real medical images but are computer-generated, helping to expand the training data (claim 1, step 3). This expanded dataset is then used to train an 'improved multi-feature fusion segmentation network,' which is a modified U-Net (claim 1, step 5). A key improvement is the addition of a 'Dilated Convolution Module' between the network's shallow and deep feature connections, which helps the network better combine fine details with broader context, making it more adaptable to different target sizes and improving accuracy (claim 1, step 5). Finally, this trained network can accurately segment new medical images, for example, precisely outlining a tumor in an MRI scan.
The clever bit
The cleverness lies in two main parts: first, using a Generative Adversarial Network (GAN) to create more training data, which helps prevent the AI from 'overfitting' to a small dataset. Second, it integrates a 'Dilated Convolution Module' into the U-Net's skip connections, allowing the network to see objects at different scales more effectively, thus improving its ability to find and outline targets of varying sizes.
What it does not cover
- Does not cover medical image segmentation methods that do not use a Generative Adversarial Network (GAN) for data enhancement.
- Does not cover U-Net based segmentation networks that lack a Dilated Convolution Module in their skip connections.
- Does not cover image segmentation tasks for non-medical images, such as segmenting objects in photographs or satellite imagery.
- Does not cover medical image segmentation techniques that do not rely on a U-Net or a multi-feature fusion segmentation network architecture.
- Does not cover methods that do not use a Dice loss function for training the segmentation network, as specified in claim 4.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
0/40
No citations yet
Claim breadth
6/20
Moderate scope
Recency
20/20
Granted within 5 years
Assignee scale
20/20
Major company or institution
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$29K – $92K
Midpoint $58K · 15.2 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
9 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
YAN, W., ZHANG, D., & Zhao, R. (2023). Improving Medical Image Segmentation with AI and Fake Data (U.S. Patent No. 11,580,646). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11580646/medical-image-segmentation-method-based-on-u-net
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Improving Medical Image Segmentation with AI and Fake Data cover?
This patent describes a method to make AI better at finding specific things in medical images, like tumors, by using computer-generated fake images to train the AI and a special network design.
Who owns patent US 11580646?
Nanjing University of Posts and Telecommunications owns this patent, granted in 2023.
When does this patent expire?
This patent is expected to expire on January 5, 2042, when the invention enters the public domain.
What problem does this patent solve?
Accurate medical image segmentation is crucial for diagnosing diseases, planning treatments, and monitoring patient progress. This method aims to improve the precision of these tasks by addressing common challenges in AI training, such as limited access to diverse medical datasets and the need for robust feature recognition. By generating synthetic data and enhancing the network's ability to fuse different levels of detail, it helps medical professionals get clearer and more reliable outlines of areas of interest, like organs or lesions.
What does this patent NOT cover?
Does not cover medical image segmentation methods that do not use a Generative Adversarial Network (GAN) for data enhancement.
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