Deep Learning Navigation for Anatomical Object Detection
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Solution Overview
Problem
Current medical imaging systems are not robust in detecting anatomical objects, especially when they exhibit large variations in anatomy, shape, and noise, making it difficult for medical professionals to quickly and accurately locate target anatomical objects during procedures like ultrasound-guided regional anesthesia.
Innovation Solution
A system utilizing deep learning networks, specifically deep convolutional neural networks or recurrent neural networks, to automatically detect and identify anatomical objects from real-time images, providing navigational directions to medical professionals by mapping relative spatial and temporal locations of the objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional imaging systems are used to detect anatomical objects, then the system complexity remains low, but the detection reliability and accuracy deteriorate due to large variations in anatomy, shape, and noise
Solution Approach 1:
The patent replaces traditional mechanical imaging detection methods with deep learning-based automated detection. The system uses neural networks to automatically identify anatomical objects in medical images, substituting manual interpretation and traditional image processing algorithms with intelligent automated systems that can handle anatomical variations and noise more effectively
Solution Approach 2:
The patent changes the detection parameters by using deep learning models that can adapt to varying anatomical parameters. The system learns from training data to recognize patterns across different anatomical configurations, allowing it to maintain high detection reliability despite variations in anatomy, shape, and imaging conditions
2Productivity
If traditional imaging systems are used, then the ease of operation is maintained, but the productivity and speed of locating target objects deteriorate
Solution Approach 1:
The system performs self-service by automatically detecting and identifying anatomical objects without requiring manual intervention. The deep learning model autonomously processes medical images, locates target objects, and provides detection results, eliminating the need for operators to manually search through images and improving productivity while maintaining ease of use through automated workflows
3Measurement precision
If manual detection methods are used, then the device complexity is low, but the measurement precision and accuracy of anatomical object location deteriorate
Solution Approach 1:
The patent replaces manual detection methods with automated deep learning-based detection systems. The neural networks provide precise localization of anatomical objects by learning from large datasets, achieving high measurement precision while the automated nature of the system manages the complexity through standardized processing pipelines
Data Source
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AI summary
The present invention is directed to a system and method for providing navigational directions to a user to locate a target anatomical object during a medical procedure via a medical imaging system. The method includes selecting an anatomical region surrounding the object; generating a plurality of real-time two-dimensional images of scenes from the anatomical region and providing the plurality of images to a controller; developing and training a deep learning network to automatically detect and identify the scenes from the anatomical region; automatically mapping each of the plurality of images from the anatomical region based on a relative spatial location and a relative temporal location of each of the identified scenes in the anatomical region via the deep learning network; and providing directions to the user to locate the object during the medical procedure based on the relative spatial and temporal locations of each of the identified scenes.