3D Image Volume Segmentation via 2D Slice Rendering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Segmenting 3D image volumes is often difficult, tedious, and time-consuming due to the need for extensive user input, particularly when methods require segmenting each slice individually or using multiple segmented slices as a seed for the remainder of the volume.
Innovation Solution
A method and system that involve obtaining a 3D volume dataset, rendering it based on 3D rendering settings, creating a 2D segmentation, and using both the 2D and 3D rendering settings to define a 3D region of interest within the dataset, thereby reducing user input and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each slice of the 3D image volume is segmented one at a time, then the segmentation can be performed systematically, but the process becomes difficult, tedious, and time-consuming requiring greater user input
Solution Approach 1:
The patent divides the 3D volume segmentation task into multiple 2D slice segmentations. Each slice is segmented independently using 2D segmentation tools, and the resulting 2D segmentations are then combined to form the complete 3D segmentation. This approach allows users to work with simpler 2D interfaces while automatically achieving comprehensive 3D coverage, reducing both time and user input requirements compared to traditional 3D segmentation methods.
Solution Approach 2:
The patent transforms the 3D segmentation problem into a series of 2D segmentation problems. By rendering 3D volume data as 2D slices and performing segmentation in 2D space, the system leverages the simplicity of 2D interaction while maintaining 3D accuracy. The 2D segmentations are then projected back into 3D space to create the final segmented volume, effectively using dimensionality conversion to reduce complexity and user burden.
2Extent of automation
If several slices are segmented manually or semi-automatically to use as a seed for the remainder of the volume, then some automation is achieved, but the method still suffers from difficulty, tediousness, and time-consuming requirements
Solution Approach 1:
The patent performs preliminary 2D segmentation on multiple slices before combining them into a 3D segmentation. By segmenting individual 2D slices first using automated or semi-automated 2D tools, the system prepares the necessary components that are then automatically assembled into the final 3D segmentation. This preliminary 2D processing reduces the complexity of the overall 3D task and minimizes the user input required for the final 3D result.
Solution Approach 2:
The system enables self-service 3D segmentation by automatically combining multiple 2D segmentations into a coherent 3D segmentation without requiring additional user intervention. Once the 2D slices are segmented, the system autonomously integrates these segmentations into the 3D volume, performing the combination operation without further user input. This self-service capability significantly reduces the operational burden on users while maintaining high automation levels.
Data Source
AI summary
A method is provided for segmenting three-dimensional (3D) image volumes. The method includes obtaining a 3D volume data set corresponding to an imaged volume, rendering at least a portion of the 3D volume data set based on 3D rendering settings, and creating a two-dimensional (2D) segmentation of the rendered 3D volume data set. The method further includes segmenting the 3D volume data set using the 2D segmentation and the 3D rendering settings to define a 3D region of interest within the 3D volume data set.