Radiology Report Generation Through Tumor Segmentation and Visual Mapping
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Solution Overview
Problem
Manual interpretation of medical imaging is time-consuming, expensive, and subjective, making it difficult for non-expert clinicians and patients to interpret radiology reports, which often lack important quantitative measurements.
Innovation Solution
A method for generating radiology reports using computer systems to visualize tumors from medical images, including CT scans, by segmenting tissue types, generating masks, and creating visual representations of tumor and skeletal structures, along with quantitative measurements, to provide clear, interpretable visuals of tumor burdens and progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual interpretation of medical imaging is used, then radiologists can provide expert diagnosis, but the process is time-consuming and expensive
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between the medical images and the radiologist. This system performs preliminary segmentation, quantification, and measurement of anatomical structures, generating structured data that assists radiologists in making faster and more consistent diagnoses without replacing expert judgment
Solution Approach 2:
The system performs preliminary analysis of medical images by automatically segmenting tissues, calculating volumes, and measuring anatomical parameters before the radiologist reviews the case. This preliminary quantification work reduces the time radiologists need to spend on manual measurements while maintaining diagnostic accuracy
2Measurement precision
If manual measurements are taken for tumor quantification, then quantitative data can be obtained, but the process is time-consuming and measurements are often missing
Solution Approach 1:
The patent replaces manual mechanical measurement methods with automated computer-based image analysis algorithms. The system uses digital image processing to automatically segment tumor regions, calculate volumes, and track changes over time, eliminating the need for time-consuming manual caliper measurements and providing consistent quantitative data
Solution Approach 2:
The automated analysis system performs self-service by independently segmenting anatomical structures and calculating measurements without requiring radiologist intervention for each measurement. The system automatically handles routine quantification tasks, allowing radiologists to focus on interpretation and complex cases
3Loss of information
If detailed radiology reports are generated, then comprehensive diagnostic information is provided, but the reports are difficult for non-expert clinicians and patients to interpret
Solution Approach 1:
The patent segments the radiology report into distinct components: automated quantitative measurements, structured findings, and interpretive conclusions. The quantitative data (volumes, densities, measurements) are separated and presented in standardized formats, making it easier for different users to find and understand relevant information without wading through unstructured text
Solution Approach 2:
The system transforms complex imaging data into standardized quantitative parameters with clear units and reference ranges. By converting subjective descriptive terms into objective numerical measurements (e.g., tumor volume in cm³, density in Hounsfield units), the report becomes more interpretable for non-expert clinicians and patients while retaining complete diagnostic information
Data Source
AI summary
System and methods for visualizing a cancer in a subject are provided herein. Medical images of a three-dimensional region of interest of the subject are obtained. The medical images are segmented by assigning labels corresponding to tissue types for each of a plurality of sets of one or more pixels in the medical images. Masks are generated for the tissue types based on the segmentation labels. A visual representation of tumor tissue is generated based on a corresponding mask for the tumor tissue. A visual representation of a skeleton of the subject is also generated based on the medical images. The visual representation of the tumor tissue and the visual representation of the skeleton of the subject is displayed in a single image, in a same spatial orientation as in the set of medical images, and at a same relative size as in the set of medical images.