18156971. 3D INTERACTIVE ANNOTATION USING PROJECTED VIEWS simplified abstract (GE PRECISION HEALTHCARE LLC)

3D INTERACTIVE ANNOTATION USING PROJECTED VIEWS

Organization Name

GE PRECISION HEALTHCARE LLC

Inventor(s)

Vincent Morard of Chassieu (FR)

Jorge Hernandez Londono of Versailles (FR)

Nicolas Gogin of Chatenay Malabry (FR)

3D INTERACTIVE ANNOTATION USING PROJECTED VIEWS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18156971 titled '3D INTERACTIVE ANNOTATION USING PROJECTED VIEWS

Simplified Explanation

The patent application describes a method for segmenting image data, specifically in the context of medical imaging. The method involves selecting pixels from a 2D rendering of a 3D medical image, converting them back to 3D space to create a segmentation mask, and applying this mask to the original 3D image dataset.

• The method involves selecting a set of pixels from a 2D rendering of a 3D medical image.
• These selected pixels are then converted back to 3D space to create a segmentation mask.
• The segmentation mask is saved in memory or applied to the original 3D medical image dataset.
• The 2D rendering used in this process is an intensity projection rendering.

Key Features and Innovation

• Utilizes a 2D rendering of a 3D medical image dataset for segmentation.
• Retropropagates selected pixels from 2D to 3D space to create a segmentation mask.
• Enables the application of the segmentation mask to the original 3D medical image dataset.
• Specifically designed for intensity projection renderings in medical imaging.

Potential Applications

• Medical image segmentation for diagnostic purposes.
• Image analysis in research settings.
• Automated image processing in healthcare.

Problems Solved

• Efficient segmentation of complex 3D medical image datasets.
• Improved accuracy in identifying and isolating specific areas of interest in medical images.

Benefits

• Enhanced visualization and analysis of medical imaging data.
• Streamlined segmentation process for medical professionals.
• Increased efficiency in medical image interpretation.

Commercial Applications

Medical Imaging Software Development

This technology could be integrated into medical imaging software to improve segmentation capabilities and enhance diagnostic accuracy in healthcare settings.

Prior Art

There may be prior art related to image data segmentation methods in medical imaging, particularly in the field of computer-aided diagnosis and image processing algorithms.

Frequently Updated Research

Research on advanced image segmentation techniques in medical imaging is constantly evolving, with new methods and algorithms being developed to improve accuracy and efficiency in image analysis.

Questions about Image Data Segmentation

How does this method compare to traditional manual segmentation techniques in medical imaging?

This method offers a more automated and efficient approach to segmentation compared to manual methods, saving time and reducing the potential for human error.

What are the potential limitations of using intensity projection renderings for image segmentation in medical imaging?

Intensity projection renderings may not always capture all relevant details in a 3D medical image, potentially leading to inaccuracies in the segmentation process.

Original Abstract Submitted

Various methods and systems are provided for image data segmentation. In one example, a method includes receiving a first segmentation input selecting a first set of pixels of a two-dimensional (2D) projected rendering, the 2D projected rendering generated from a 3D medical image dataset, retropropagating the selected first set of pixels to 3D space based on a mapping between the 2D projected rendering and the 3D medical image dataset to form a 3D segmentation mask, and saving the 3D segmentation mask in memory and/or applying the 3D segmentation mask to the 3D medical image dataset, wherein the 2D projected rendering is an intensity projection rendering.