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LVONet: automatic classification model for large vessel occlusion based on the difference information between left
Yuqi Ma1, Shanxiong Chen1, Hailing Xiong2
1College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China.
Insights
A new deep learning model accurately classifies large vessel occlusion (LVO) in the brain by analyzing hemispheric differences. This advancement aids in assessing stroke conditions and improving treatment strategies for intracranial vessel occlusions.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Stroke, a leading cause of death, often results from intracranial vessel occlusion.
- Accurate classification of these occlusions is crucial for effective treatment, but visual similarities pose challenges.
- Current automated methods struggle with variations in vessel appearance and occlusion degrees.
Purpose of the Study:
- To develop an advanced automatic classification model for large vessel occlusion (LVO).
- To leverage hemispheric symmetry for improved detection of intracranial vessel occlusions.
- To enhance the accuracy of automated LVO classification in challenging clinical scenarios.
Main Methods:
- A dual-branch attention module was employed to capture long-range dependencies and focus on vessel-specific features.
- A differential information classification module was designed to fuse inter-hemispheric vessel feature differences.
- A novel cooperative learning loss function was introduced to optimize feature discrimination.
Main Results:
- The proposed model achieved high performance on an intracranial LVO dataset.
- Achieved classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91%, and Macro-F1 score of 87.13%.
- Outperformed existing state-of-the-art deep learning models in LVO classification.
Conclusions:
- The developed model effectively identifies occluded vessel regions by adaptively focusing on relevant features.
- The approach demonstrates robustness in handling high inter-class similarity and intra-class variability.
- This method significantly improves the performance of large vessel occlusion classification for stroke assessment.
Abstract:
Objective.Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions. However, due to the visual similarities in shape and size among different vessels and variations in the degree of vessel occlusion, the automated classification of intracranial vessel occlusions remains a challenging task. Our study proposes an automatic classification model for large vessel occlusion (LVO) based on the difference information between the left and right hemispheres.Approach.Our approach is as follows. We first introduce a dual-branch attention module to learn long-range dependencies through spatial and channel attention, guiding the model to focus on vessel-specific features. Subsequently, based on the symmetry of vessel distribution, we design a differential information classification module to dynamically learn and fuse the differential information of vessel features between the two hemispheres, enhancing the sensitivity of the classification model to occluded vessels. To optimize the feature differential information among similar vessels, we further propose a novel cooperative learning loss function to minimize changes within classes and similarities between classes.Main results.We evaluate our proposed model on an intracranial LVO data set. Compared to state-of-the-art deep learning models, our model performs optimally, achieving a classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91% and Macro-F1 score of 87.13%.Significance.This method can adaptively focus on occluded vessel regions and effectively train in scenarios with high inter-class similarity and intra-class variability, thereby improving the performance of LVO classification.
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