One-Shot Anatomy Localization via Vision Transformers
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Solution Overview
Problem
Current medical imaging techniques require manual or algorithmic segmentation for anatomical localization, which is time-consuming and inefficient, especially when dealing with changes in anatomy or imaging data.
Innovation Solution
A computer-implemented method using a trained vision transformer model to perform one-shot anatomy localization by processing medical images and template images, extracting patch and pixel-level features, and clustering them to assign anatomical labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or algorithmic segmentation is used for anatomical localization, then anatomical landmarks can be identified, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by using a pre-trained vision transformer model that has already learned anatomical features from unlabeled medical images. This pre-training enables the model to perform one-shot anatomy localization without requiring time-consuming manual annotation or repeated fine-tuning for each new anatomy type, thus resolving the time efficiency problem while maintaining localization accuracy
Solution Approach 2:
The invention creates a copy of the pre-trained vision transformer model that can be applied to new medical images without requiring the original training process to be repeated. This allows the system to quickly localize anatomical landmarks in new images by leveraging the knowledge already encoded in the model, eliminating the need for repeated manual segmentation while preserving localization precision
2Measurement precision
If fine-tuning through segmentation algorithms is performed for each anatomy change, then anatomical localization can be achieved, but the process must be repeated repeatedly
Solution Approach 1:
The vision transformer model is designed with universality to handle multiple anatomy types and imaging modalities simultaneously. The model processes medical images by dividing them into patches and using self-attention mechanisms to identify anatomical landmarks across different body parts and imaging conditions without requiring anatomy-specific fine-tuning, thus improving productivity while maintaining detection accuracy
Solution Approach 2:
The system achieves adaptability to different anatomy types by changing parameters within the unified model framework rather than retraining the entire model. The vision transformer model adjusts its internal parameters and attention weights dynamically based on the input image characteristics, enabling it to accurately detect anatomical landmarks across diverse medical imaging scenarios without repeated fine-tuning processes
3Measurement precision
If supervised segmentation with ground truth annotation is used, then accurate anatomical labeling can be obtained, but manual annotation is required
Solution Approach 1:
The vision transformer model performs self-service by learning from unlabeled medical images through self-supervised pre-training. The model automatically identifies anatomical patterns and features without requiring manual ground truth annotation, then uses this learned knowledge to accurately label anatomical structures in new images. This eliminates the need for complex manual annotation processes while maintaining labeling accuracy
Solution Approach 2:
The system performs preliminary action by conducting unsupervised pre-training on large sets of unlabeled medical images before deployment. This pre-training phase enables the model to learn anatomical features and relationships without manual annotation, preparing it for accurate one-shot anatomy localization in clinical applications while avoiding the operational complexity of supervised annotation
Data Source
AI summary
A method for performing one-shot anatomy localization includes obtaining a medical image of a subject. The method includes receiving a selection of both a template image and a region of interest within the template image, wherein the template image includes one or more anatomical landmarks assigned a respective anatomical label. The method includes inputting both the medical image and the template image into a trained vision transformer model. The method includes outputting from the trained vision transformer model both patch level features and image level features for both the medical image and the template image. The method still further includes interpolating pixel level features from the patch level features for both the medical image and the template image. The method includes utilizing the pixel level features within the region of interest of the template image to locate and label corresponding pixel level features in the medical image.