2D Image Integration for 3D Organ Range Mapping
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Solution Overview
Problem
Existing methods for determining the imaging range in medical imaging, such as CT and MRI, are time-consuming and dependent on operator skill, leading to variations in accuracy due to manual setting and lack of consideration for tomographic image positions.
Innovation Solution
An image processing device and method that uses a derivation model to derive existence range information for structures in two-dimensional images and integrates this information using an integration model, considering spatial positions across multiple images to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setting of imaging range is performed by operator while viewing scout image, then operator can determine imaging range, but it requires time and setting accuracy varies depending on operator ability and experience
Solution Approach 1:
The system performs automatic imaging range determination using AI models and algorithms, enabling the system to serve itself without operator intervention. The processor automatically derives three-dimensional positions of organs from scout images and determines the imaging range, eliminating the need for manual operator setting while improving accuracy consistency.
Solution Approach 2:
The manual mechanical process of operator viewing and setting is replaced with an automated computational system. The processor uses image processing algorithms and machine learning models to automatically determine the imaging range, substituting human manual operation with automated mechanical/computational processes.
2Measurement precision
If existing method derives bounding box for each tomographic image and integrates position coordinates by obtaining statistical value, then three-dimensional position can be estimated, but coordinate position accuracy is insufficient because the method does not consider the position of the tomographic image
Solution Approach 1:
The method transitions from two-dimensional bounding box derivation in individual tomographic images to three-dimensional coordinate determination by integrating across multiple dimensions. The processor derives three-dimensional position coordinates (x, y, z) by considering the spatial relationships and positions across multiple tomographic images, adding the depth dimension to the integration process.
Solution Approach 2:
The processor acts as an intermediary that integrates the bounding box information from multiple tomographic images. Instead of directly combining statistical values, the processor first derives three-dimensional position coordinates for each image, then integrates these coordinated positions while considering the spatial relationships between images, providing a mediating transformation that improves accuracy.
Data Source
AI summary
An image processing device includes a processor, in which the processor is configured to: derive, for each of a plurality of two-dimensional images including a structure and having a spatial connection, existence range information for defining a spatial existence range of the structure in a direction intersecting the two-dimensional images, by using a derivation model; and integrate the existence range information derived for each of the plurality of two-dimensional images, based on spatial positions of the plurality of two-dimensional images, by using an integration model.