Weakly Supervised 3D Image Segmentation via Simulated Point Selections
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Solution Overview
Problem
The manual annotation of 3D medical images for segmentation is highly time-consuming and requires extensive expert effort, making it impractical to generate large and high-quality datasets for training machine learning models.
Innovation Solution
A method using weakly supervised segmentation models to simulate positive and negative point selections in adjacent image slices, allowing for the efficient delineation of 3D regions of interest by extending initial annotations made on one image slice to multiple adjacent slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation is used to generate ground truth segmentation masks for training ML models, then high-quality training data is obtained, but the time and expert effort required becomes prohibitive
Solution Approach 1:
The system performs preliminary automated segmentation using a pre-trained ML model to generate initial segmentation masks before manual refinement. This preliminary action creates a head start, reducing the amount of manual work needed from experts who only need to correct rather than create annotations from scratch.
Solution Approach 2:
An automated ML-based segmentation system acts as an intermediary between no annotation and full manual annotation. This intermediary generates initial segmentation masks that serve as a bridge, requiring minimal expert intervention to achieve high-quality ground truth annotations.
2Manufacturing precision
If detailed 3D segmentation masks are manually annotated for multiple ROIs in volumetric images, then high-quality training data is produced, but the annotation process becomes extremely time-consuming
Solution Approach 1:
The system applies automated segmentation to the entire 3D volume rather than requiring manual annotation of every ROI. The ML model performs excessive automated segmentation that may include some errors, but this is far more efficient than partial manual annotation, and subsequent refinement processes correct the errors.
Solution Approach 2:
The ML segmentation model serves itself by automatically generating segmentation masks without requiring expert annotators to perform the labor-intensive task of delineating every ROI boundary in 3D space. The system annotates its own training data with minimal human oversight.
3Loss of information
If comprehensive manual annotation of all image slices is performed, then complete 3D segmentation coverage is achieved, but the resource requirements become unsustainable
Solution Approach 1:
The system performs preliminary automated annotation on representative slices or the entire volume, creating initial segmentation coverage before any manual refinement. This preliminary 3D segmentation ensures complete coverage is achieved automatically, requiring minimal expert resources for subsequent refinement.
Solution Approach 2:
The patent replaces the mechanical process of manual slice-by-slice annotation with an automated ML-based segmentation system. This substitution maintains complete 3D coverage while eliminating the need for sustained expert resources across all image slices.
Data Source
AI summary
Various methods and systems are provided for segmenting a three-dimensional (3D) region of interest (ROI) from an image sequence. In one embodiment, a method includes receiving a positive point selection or negative point selection for an ROI in a first image slice of an image sequence, mapping the first image slice and the positive point selection or negative point selection to a first segmentation mask of the ROI using a weakly supervised segmentation model, simulating positive and negative point selections in a second image slice based on the first segmentation mask to produce a plurality of simulated positive and negative point selections, wherein the second image slice is adjacent to the first image slice, and mapping the second image slice and the plurality of simulated positive and negative point selections to a second segmentation mask for the ROI in the second image slice using the weakly supervised segmentation model.