Image Slice Classification via Localizer Bounding Boxes
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Solution Overview
Problem
Conventional methods for assigning multiple image slices to corresponding regions in medical imaging are either time-consuming when done manually or computationally expensive when automated, requiring efficient classification techniques to improve computational efficiency without sacrificing accuracy.
Innovation Solution
A deep learning model-based method that uses a localizer image to extract features, map them to bounding boxes, resolve borders to produce non-intersecting regions, and correlate coordinates to assign image slices to target regions, reducing computational complexity and avoiding assignment conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assignment of image slices to regions is performed, then classification accuracy can be maintained, but time consumption increases significantly
Solution Approach 1:
The patent uses a deep learning model to create an automated copying of the manual classification process. The model learns from manually annotated training data and reproduces the classification decisions automatically, achieving both high accuracy and speed. The neural network copies the pattern recognition skills of radiologists and applies them consistently across all image slices without human intervention.
Solution Approach 2:
The patent replaces the mechanical manual assignment process with an automated deep learning system. Instead of radiologists visually inspecting and assigning each image slice, the system uses neural networks to automatically classify image slices based on learned patterns, substituting human cognitive processing with computational algorithms.
2Loss of time
If automated classification of image slices is performed using conventional methods, then time consumption is reduced, but computational expense increases significantly
Solution Approach 1:
The patent segments the classification task by first identifying a small set of representative image slices that capture the essential characteristics of each anatomical region. Instead of processing all image slices individually, the system selects a subset that is sufficient for accurate region classification, dramatically reducing the computational workload while maintaining effectiveness.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of image slices rather than all slices. The deep learning model identifies and processes a representative sample of slices that provides sufficient information for accurate region classification, avoiding the excessive computational expense of processing every single slice in detail.
3Extent of automation
If machine learning models are used to classify each image slice, then automation is achieved, but computational complexity increases
Solution Approach 1:
The patent segments the computational task by dividing image slices into distinct anatomical regions based on spatial location and characteristics. The deep learning model processes slices in organized groups corresponding to different body regions, which simplifies the computational architecture and reduces overall complexity compared to processing all slices uniformly.
Solution Approach 2:
The patent reduces computational complexity by applying partial processing to only the essential image slices needed for region identification. Rather than applying full classification algorithms to every slice, the system identifies and processes only the representative slices that provide sufficient information, reducing the overall computational burden while maintaining automation.
Data Source
AI summary
Various methods and systems are provided for automatically classifying a plurality of image slices using body region bounding boxes identified from a localizer image. In one embodiment, a localizer image may be mapped to a plurality of bounding boxes, corresponding to a plurality of body regions, using a trained machine learning model. Coordinates of the plurality of bounding boxes may be used to determine body region boundaries, such that the body regions are non-intersecting and coherent. The body regions identified in the localizer image may then be correlated to image slice ranges, and image slices within each image slice range may be labeled as belonging to the corresponding body region.