Connected CNN Joint Training for Brain Lesion Segmentation
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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 segmentation accuracy.
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 models produce increased false positives and fail to capture the relationship between different lesion types
Solution Approach 1:
The patent combines multiple separate deep learning models into a single unified model that simultaneously segments both T1 unenhancing and Gd-enhancing lesions. This unified model shares common feature extraction layers and only diverges in the final segmentation layers, allowing the model to learn relationships between different lesion types while reducing false positives through joint training and shared contextual understanding.
Solution Approach 2:
The unified model is architecturally segmented into shared feature extraction components and specialized segmentation components. The shared components process input images to extract common features, while separate segmentation heads generate predictions for different lesion types, enabling both generalization and specialization within a single model framework.
2Device complexity
If a single deep learning model is used for multi-class lesion segmentation, then the model structure becomes simpler, but the model struggles to capture the disjoint nature and significant shape differences between lesion types
Solution Approach 1:
The model architecture is segmented into modular components: shared convolutional layers for feature extraction and specialized segmentation layers for each lesion type. This modular structure maintains simplicity in the common processing pipeline while enabling specialized handling of different lesion characteristics through separate output heads.
Solution Approach 2:
Different parts of the model have different functions: the shared lower layers perform general feature extraction applicable to all lesion types, while the specialized upper layers perform type-specific segmentation. This local differentiation allows the model to maintain overall simplicity while achieving high precision for diverse lesion characteristics.
3Measurement precision
If manual annotation by radiologists is used, then high accuracy ground truth can be obtained, but the process is time-consuming and shows high variability across different radiologists
Solution Approach 1:
The deep learning model performs automated lesion segmentation without requiring manual radiologist annotation for each new case. The model learns from a training dataset with ground truth annotations and then independently segments lesions in new images, eliminating the need for time-consuming manual annotation while maintaining consistent accuracy across all cases.
Solution Approach 2:
The model creates a digital copy of the radiologist's segmentation expertise through training on annotated data. Once trained, the model replicates high-quality segmentation results automatically, replacing the need for repeated manual annotation while preserving the accuracy standards established during training.
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.