3D Image Detection via Superpixel Grids and Neural Networks
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Solution Overview
Problem
Current deep learning technologies face challenges when processing 3D medical images due to high memory usage and difficulty in training neural networks, as 3D images require larger GPU memory and have complex structures, making it hard to achieve efficient and precise detection.
Innovation Solution
The method involves layering 3D images into 3D subimages, performing intra-layer clustering to create superpixel grids, and using a neural network with a first classifier for low-precision detection and a second classifier for high-precision detection on the superpixel grids, reducing memory usage and simplifying training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning technologies are used to process 3D medical images directly, then detection precision can be improved, but memory usage increases significantly and training becomes difficult
Solution Approach 1:
The patent divides a 3D medical image into multiple 2D slices or sub-images along different axes. Each 2D slice is processed independently through the neural network, significantly reducing the memory required for each processing step compared to loading the entire 3D volume at once. After processing, the results are integrated to form the final detection output.
Solution Approach 2:
The patent implements a hierarchical processing structure where 3D images are decomposed into 2D slices, which are further processed through multiple neural network layers. The detection results from multiple scales and resolutions are nested and integrated, allowing the system to maintain high detection precision while managing memory efficiently through multi-scale feature fusion.
2Measurement precision
If deep learning technologies are used to process 3D medical images directly, then detection precision can be improved, but training difficulty increases due to complex structures
Solution Approach 1:
By segmenting the 3D image processing into separate 2D slice processing steps, the patent simplifies the training process. Each 2D slice can be processed through standard 2D convolutional neural networks, which are well-established and easier to train compared to custom 3D network architectures. This segmentation approach leverages existing 2D deep learning tools and techniques.
Solution Approach 2:
The patent performs preliminary processing of 3D images by generating 2D slices and potentially applying preprocessing operations such as normalization or feature extraction before feeding them into the neural network. This preliminary action simplifies the subsequent training process by preparing data in a format that is more amenable to standard neural network architectures.
3Measurement precision
If high-precision detection is performed on all 3D subimages, then detection precision is improved, but detection speed decreases due to increased processing time
Solution Approach 1:
The patent applies different processing strategies to different regions of the 3D image based on their importance. High-precision detection is applied to regions of interest where objects are detected, while other regions may use lower computational overhead. This local quality approach ensures high detection precision for critical areas while maintaining overall detection speed.
Solution Approach 2:
The patent performs high-precision detection only on necessary 2D slices or regions rather than all slices uniformly. By identifying and focusing computational resources on slices that contain or are likely to contain objects of interest, the system achieves high detection precision for relevant areas while reducing overall processing time and maintaining detection speed.
Data Source
AI summary
The present disclosure provides a method and an apparatus for detecting a 3D image, an electronic device, and a computer-readable medium. The method for detecting a 3D image includes layering a 3D image to obtain at least one 3D subimage. The 3D subimage contains a plurality of 2D images. The method includes performing an intra-layer clustering on the 3D subimage to obtain a superpixel grid. The method includes inputting the superpixel grid into a neural network for detecting. The method includes detecting, in response to detecting an object in the superpixel grid, the 3D subimage forming the superpixel grid containing the object to obtain and output a detection result.