Medical Imaging Segmentation for 3D Print Quality
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Solution Overview
Problem
Current 3D printing systems face challenges in converting medical imaging data into suitable 3D printer input data, often resulting in poor print quality due to neglecting printer characteristics, leading to issues like weak sections, inaccurate parts, and 'pixelized' models.
Innovation Solution
A system and method that segment imaging data to produce masks representing objects to be printed, evaluate printability, and adjust masks according to printer definition data to ensure compatibility and quality, converting the masks into 3D printer input data that accounts for specific printer specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image data is directly converted to 3D printer input data without segmentation and evaluation, then the conversion process is simple and fast, but the print quality deteriorates with weak sections, inaccuracies, and pixelated models
Solution Approach 1:
The patent applies segmentation by dividing the image data conversion process into distinct stages: initial segmentation of imaging data to produce masks, evaluation of printability, and adjustment of masks. This segmented approach improves print quality by systematically addressing different aspects of the conversion process while managing complexity through structured organization of conversion steps.
Solution Approach 2:
The patent implements preliminary action by performing segmentation and printability evaluation before the actual conversion to printer input data. Masks are created and evaluated in advance, allowing problems to be identified and corrected before final conversion, thereby improving print quality without compromising the speed of the final conversion step.
2Manufacturing precision
If printer characteristics are neglected during data conversion, then the conversion process is simpler, but the resulting 3D models have inaccuracies and weak sections
Solution Approach 1:
The patent applies local quality by adjusting masks based on specific printer characteristics and settings. The evaluation and adjustment processes tailor the conversion to the particular printer being used, ensuring optimal print quality for that specific device while maintaining a relatively simple overall conversion framework.
Solution Approach 2:
The patent implements parameter changes by modifying mask parameters during the evaluation and adjustment phase based on printer definition data. This allows the conversion process to adapt to different printer capabilities and settings, improving model accuracy while keeping the conversion process manageable through automated parameter adjustment.
3Manufacturing precision
If image data is segmented and evaluated for printability, then the print quality improves, but the conversion time and processing complexity increase
Solution Approach 1:
The patent applies preliminary action by performing segmentation and evaluation steps before final conversion. While this adds processing time, it allows for proactive identification and correction of issues, potentially reducing rework and improving overall efficiency by catching problems early in the workflow.
Solution Approach 2:
The patent implements self-service through automated evaluation and adjustment processes that assess printability and correct issues without manual intervention. This automation reduces the time penalty associated with detailed segmentation and evaluation, as the system performs these tasks efficiently without requiring extensive human oversight.
Data Source
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AI summary
A system and method for converting imaging data, for example, medical imaging data, to three-dimensional printer data. Imaging data may be received describing for example a three-dimensional volume of a subject or patient. Using printer definition data describing a particular printer, 3D printer input data may be created from the imaging data describing at least part of the three-dimensional volume.