Radiology Report Generation Through Tumor Segmentation and 3D Visuals
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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 that includes segmenting medical images to visualize tumor burdens and generate quantitative measurements, using a computer system to create visual representations of tumor and skeletal structures from CT scans, and display them alongside relevant measurements and timelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual interpretation of medical imaging is performed by radiologists, then diagnostic accuracy and professional judgment are improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between the medical imaging data and the radiologist. This system performs preliminary segmentation, quantification, and measurement of anatomical structures, generating a draft report that the radiologist then reviews and finalizes. The intermediary handles time-consuming manual measurement tasks while preserving radiologist oversight for diagnostic accuracy.
Solution Approach 2:
The system enables self-service by automatically performing image segmentation, volume calculation, and report generation without requiring radiologist intervention for these specific tasks. The automated pipeline processes imaging data independently, extracting quantitative measurements and generating structured reports that reduce the radiologist's workload to review and validation only.
2Measurement precision
If manual measurements and quantitative analysis are performed, then measurement precision and detail are improved, but productivity and efficiency decrease
Solution Approach 1:
The patent replaces the mechanical manual measurement process with an automated computational system. Software algorithms automatically segment images, calculate volumes, and perform quantitative analysis, substituting the radiologist's manual tracing and measurement activities. This maintains measurement precision through algorithmic consistency while dramatically improving productivity by eliminating time-consuming manual operations.
3Loss of information
If detailed radiology reports with comprehensive measurements are generated, then information completeness is improved, but ease of interpretation for non-experts deteriorates
Solution Approach 1:
The patent segments the radiology report into distinct components: a detailed technical section with comprehensive measurements for experts, and a simplified visual summary with key findings for non-experts. The system separates quantitative data from interpretive commentary, allowing each audience to access the appropriate level of detail without being overwhelmed by unnecessary information.
Solution Approach 2:
The report structure applies local quality by providing different levels of information density in different sections. The executive summary uses simplified language and visual aids for patient understanding, while the full report contains detailed measurements and technical analysis for clinician review. Each section is optimized for its specific audience's needs.
4Productivity
If automated systems are used for radiology report generation, then productivity and time efficiency are improved, but measurement precision and reliability may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where automated measurements are validated against established anatomical ranges and consistency checks. The automated report generation includes quality control steps that flag unusual measurements for review, and the radiologist's final approval serves as a feedback loop to verify automated analysis accuracy before report finalization.
Data Source
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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.