Jointly Trained CNNs for 3D MRI Brain Lesion Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep learning models struggle to accurately segment T1 unenhancing and Gd-enhancing lesions in MRI images due to their disjoint nature and significant differences in shape characteristics, leading to increased false positives and challenges in multi-class settings.
Innovation Solution
A joint training approach using multiple connected convolutional neural networks (CNNs) to generate parallel segmentation masks for T1 unenhancing and Gd-enhancing lesions, sharing features across cross-network connections to improve accuracy and reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate deep learning models are used to segment T1 unenhancing and Gd-enhancing lesions, then each model can be optimized for its specific lesion type, but the segmentation accuracy decreases and false positives increase due to the disjoint nature and significant differences in shape characteristics between lesion types
Solution Approach 1:
The patent combines multiple separate deep learning models into a single unified model that segments both T1 unenhancing and Gd-enhancing lesions simultaneously. This unified model shares common feature extraction layers while having separate segmentation heads for different lesion types, allowing it to learn joint representations that reduce false positives while maintaining accuracy for both lesion types.
Solution Approach 2:
The unified model is segmented into distinct functional components: shared feature extraction layers that learn common patterns, and separate segmentation branches that specialize in different lesion types. This architectural segmentation allows the model to handle the disjoint nature of different lesion types while benefiting from shared knowledge.
2Measurement precision
If manual annotation by radiologists is used, then high accuracy segmentation can be achieved, but the processing efficiency decreases and consistency varies across different radiologists
Solution Approach 1:
The patent replaces the mechanical process of manual radiologist annotation with an automated deep learning system. The unified model processes MRI images automatically, generating segmentation masks without human intervention, thereby dramatically improving processing efficiency while maintaining consistent accuracy across all cases.
Solution Approach 2:
The model learns from manually annotated training data (ground truth) and creates automated segmentation predictions that replicate the quality and consistency of expert radiologist annotations. The training process involves copying the segmentation patterns established by radiologists into the model's decision-making process.
3Device complexity
If traditional single-model approaches are used for multi-class lesion segmentation, then the model complexity is lower, but the ability to capture shape characteristics and reduce false positives deteriorates
Solution Approach 1:
The unified model employs dynamic feature sharing where the same feature extraction layers adapt their learned representations based on the specific lesion type being segmented. The model dynamically routes features to appropriate segmentation branches, allowing it to capture shape characteristics specific to each lesion type while maintaining a single model structure.
Data Source
AI summary
Embodiments disclosed herein generally relate to connected machine learning models with joint training for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) image, wherein the three-dimensional MRI image depicts a region of a brain of a subject, wherein the region of the brain includes at least a first type of lesions and a second type of lesions; inputting the three-dimensional MRI image into a machine-learning model comprising a first convolutional neural network and a second convolutional neural network; generating a first segmentation mask for the first type of lesions using the first convolutional neural network that takes as input the three-dimensional MRI image; generating a second segmentation mask for the second type of lesions using the second convolutional neural network that takes as input the three-dimensional MRI image; and outputting the first segmentation mask and the second segmentation mask.