Unified Swin Transformer Training for Accurate 3D Lesion Segmentation
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Solution Overview
Problem
Current methods for lesion segmentation in medical imaging, particularly using CT scans, are labor-intensive, resource-intensive, and sensitive to initial training data, leading to issues like overfitting and inaccurate contours, especially when dealing with lesions of varying sizes, shapes, and locations.
Innovation Solution
A multi-stage training approach for a transformer-based machine-learning model, utilizing both 2D and 3D images, including pre-training with unlabeled 3D datasets, fine-tuning with labeled 2D datasets, and further fine-tuning with labeled 3D datasets, to enhance lesion segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D annotation is performed manually by medical professionals, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The system performs preliminary 3D reconstruction and lesion detection automatically before manual annotation, preparing pre-processed 3D volumes with detected lesion regions. This preliminary action reduces the subsequent manual annotation workload while maintaining measurement precision, as radiologists only need to verify and refine automatically detected lesions rather than annotate from scratch.
Solution Approach 2:
The patent introduces an automated image processing system as an intermediary between raw medical images and final annotated results. This intermediary performs 3D reconstruction, lesion detection, and generates preliminary annotations that serve as a bridge, reducing the direct manual annotation burden while preserving measurement accuracy through automated preprocessing steps.
2Productivity
If existing automated segmentation methods are used, then productivity is improved, but reliability worsens due to sensitivity to initial training data and overfitting
Solution Approach 1:
The training process is segmented into multiple distinct stages: unsupervised pre-training on unlabeled 3D images to learn general features, supervised fine-tuning on labeled 2D images for lesion detection, and final refinement on labeled 3D images for accurate segmentation. This segmentation of training phases allows the model to build robust features progressively, reducing overfitting and improving reliability while maintaining automation.
Solution Approach 2:
The system performs unsupervised pre-training on large unlabeled 3D image datasets before supervised training. This preliminary action enables the model to learn general anatomical features and image representations without being biased by limited labeled data, thereby improving reliability and reducing sensitivity to initial training data distribution while maintaining automated segmentation capability.
3Ease of operation
If single-diameter measurement on single slice is used, then ease of operation is improved, but measurement precision worsens
Solution Approach 1:
The system automatically performs 3D reconstruction from multiple 2D slices, transitioning from single-slice 2D measurement to multi-slice 3D measurement. This dimensional change enables comprehensive lesion volume calculation and multi-diameter measurement across different slices, improving measurement precision while maintaining ease of operation through automation. The system processes entire 3D volumes automatically, eliminating the need for manual single-slice selection.
Data Source
AI summary
A system and method of multi-stage training of a transformer-based machine-learning model. The system performs at least two stages of the following three stages of training: During a first stage, the system pre-trains a transformer encoder via a first machine-learning network using an unlabeled 3D image dataset. During a second stage, the system fine-tunes the pre-trained transformer encoder via a second machine-learning network via a labeled 2D image dataset. During a third stage, the system further fine-tunes the previously pre-trained transformer encoder or fine-tuned transformer encoder via a third machine-learning network using a labeled 3D image dataset.