Medical Image Summary Generation via Slab Segmentation
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Solution Overview
Problem
Radiologists face challenges in efficiently analyzing 3D medical images due to the large volume of data, leading to potential missed clinically significant findings, and similarly, real-time 2D images from procedures like colonoscopy can result in small features being overlooked during manual scanning.
Innovation Solution
The method involves dividing 3D medical images into slabs of similar 2D images and real-time 2D images into slabs, computing similarity datasets, and aggregating these into summary images that can be processed using machine learning models to enhance visibility of key findings, reducing computational resources required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiologists manually scan through all 2D slices of 3D medical images, then complete coverage of the image data is achieved, but the time required for analysis increases significantly and clinically significant findings may be missed
Solution Approach 1:
The patent segments the large volume of 2D medical images into smaller groups or stacks, processing them in manageable portions rather than as a single large dataset. This segmentation allows the system to identify and focus on regions containing clinically significant findings without requiring radiologists to manually review every individual slice, thereby reducing analysis time while maintaining detection accuracy.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between the raw 3D medical image data and the radiologist. This intermediary automatically processes the image data, identifies clinically significant findings, and presents them to the radiologist for verification, eliminating the need for manual scanning of all slices while ensuring reliable detection of important findings.
2Reliability
If all 2D images from 3D medical images are processed using machine learning models, then comprehensive analysis is achieved, but computational resources required increase significantly
Solution Approach 1:
The patent segments the image processing task by dividing the 3D medical image into multiple 2D slices and further organizing them into stacks or groups. Machine learning models are applied selectively to relevant segments rather than processing all 2D images uniformly, reducing computational resource requirements while maintaining comprehensive analysis of clinically significant findings.
Solution Approach 2:
The patent applies different processing qualities and computational resources to different regions of the medical image data based on their clinical significance. Regions containing potentially important findings receive more intensive machine learning analysis, while less critical regions are processed more efficiently or skipped, optimizing the balance between detection accuracy and computational resource usage.
3Measurement precision
If real-time 2D images from procedures like colonoscopy are manually scanned, then detailed inspection is possible, but small features are overlooked and processing time increases
Solution Approach 1:
The patent introduces an intermediary automated analysis system that processes real-time 2D images from procedures like colonoscopy. This intermediary continuously monitors the image stream, identifies small features that may be overlooked during manual scanning, and alerts the operator, thereby improving feature detection precision without significantly impacting processing speed.
Solution Approach 2:
The patent enables continuous processing of real-time 2D images through automated analysis, maintaining uninterrupted monitoring of the procedure. This continuous useful action ensures that small features are detected in real-time without the breaks and delays associated with manual scanning, improving both precision and productivity.
Data Source
AI summary
There is provided a computer implemented method for generating summary images from 3D medical images, comprising: receiving a 3D medical image, dividing the 3D medical images into a sequence of a plurality 2D images, computing a similarity dataset indicative of an amount of similarity between each pair of the plurality of 2D images, segmenting the similarity dataset into a plurality of slabs by minimizing the amount of similarity between consecutive slabs and maximizing the amount of similarity within each slab, aggregating, for each respective slab, the plurality of 2D images into a respective summary image, and presenting on a display, the respective summary image.