3D Learning Network for Automatic Target Object Detection in Medical Images
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Solution Overview
Problem
Current medical image analysis, particularly in detecting target objects from 3D images, is labor-intensive, time-consuming, and prone to errors due to the lack of 3D spatial information, relying heavily on experienced radiologists and limited by the inefficiencies of existing machine learning methods that struggle with 2D image learning.
Innovation Solution
A 3D learning network is employed to automatically detect target objects in 3D images by generating feature maps of varying scales, determining bounding boxes, and identifying parameters to locate and classify objects, such as lung nodules, using a feed-forward 3D convolutional neural network with fully connected layers transformed into fully convolutional layers for enhanced computation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiologists manually detect target objects from 3D images, then detection accuracy can be maintained through human expertise, but the process becomes tedious, time-consuming and error-prone
Solution Approach 1:
The system enables automatic detection of target objects through a 3D learning network that processes volumetric images independently, eliminating the need for manual radiologist intervention while maintaining detection accuracy through automated feature extraction and analysis
Solution Approach 2:
The patent replaces the mechanical human detection process with an automated 3D learning network system that uses computational algorithms to detect, localize, and classify target objects in medical images, thereby improving efficiency while maintaining reliability
2Extent of automation
If basic machine learning methods are used for detection, then automation is introduced, but detection accuracy remains low due to artificial feature definition
Solution Approach 1:
The patent transforms the detection approach by changing from artificial feature definition to learning-based feature extraction, where the 3D learning network automatically learns optimal features from training data, thereby improving detection accuracy while maintaining automation
Solution Approach 2:
The system replaces basic machine learning methods with a deep 3D learning network that automatically extracts features from volumetric images, eliminating the need for manual feature engineering and significantly improving detection precision
3Measurement precision
If 3D learning is implemented for target object detection, then detection accuracy improves through 3D spatial information, but substantial computation resources are needed
Solution Approach 1:
The patent segments the 3D volumetric image into multiple 2D slices that are processed individually by the learning network, reducing the computational burden of processing the entire 3D volume at once while still capturing spatial relationships through multi-scale feature maps
Solution Approach 2:
The system transforms the 3D detection problem into a series of 2D processing tasks with added spatial context, using multi-scale feature maps to recover 3D spatial information without requiring full 3D convolution operations, thereby reducing computation resource consumption
4Use of energy by moving object
If 2D image learning is used, then computation resources are reduced, but 3D spatial information is lost making detection difficult
Solution Approach 1:
The patent processes 2D image slices while incorporating 3D spatial context through multi-scale feature maps that capture spatial relationships across different depths, thereby maintaining low computational cost while preserving essential 3D spatial information for accurate detection
Data Source
AI summary
A computer-implemented method for automatically detecting a target object from a 3D image is disclosed. The method may include receiving the 3D image acquired by an imaging device. The method may further include detecting, by a processor, a plurality of bounding boxes as containing the target object using a 3D learning network. The learning network may be trained to generate a plurality of feature maps of varying scales based on the 3D image. The method may also include determining, by the processor, a set of parameters identifying each detected bounding box using the 3D learning network, and locating, by the processor, the target object based on the set of parameters.