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A novel metastatic tumor segmentation method with a new evaluation metric in clinic study
Bin Li1, Qiushi Sun2, Xianjin Fang3
1Department of Neurology, The First Hospital of Anhui University of Science and Technology, Huainan, China.
Background:
Brain metastases are the most common brain malignancies. Automatic detection and segmentation of brain metastases provide significant assistance for radiologists in discovering the location of the lesion and making accurate clinical decisions on brain tumor type for precise treatment.
Objectives:
However, due to the small size of the brain metastases, existing brain metastases segmentation produces unsatisfactory results and has not been evaluated on clinic datasets.
Methodology:
In this work, we propose a new metastasis segmentation method DRAU-Net, which integrates a new attention mechanism multi-branch weighted attention module and DResConv module, making the extraction of tumor boundaries more complete. To enhance the evaluation of both the segmentation quality and the number of targets, we propose a novel medical image segmentation evaluation metric: multi-objective segmentation integrity metric, which effectively improves the evaluation results on multiple brain metastases with small size.
Results:
Experimental results evaluated on the BraTS2023 dataset and collected clinical data show that the proposed method has achieved excellent performance with an average dice coefficient of 0.6858 and multi-objective segmentation integrity metric of 0.5582.
Conclusion:
Compared with other methods, our proposed method achieved the best performance in the task of segmenting metastatic tumors.
Insights
A new method, DRAU-Net, improves brain metastasis segmentation using novel attention mechanisms. This advancement aids radiologists in precise diagnosis and treatment planning for brain malignancies.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Brain metastases represent the most frequent brain malignancies.
- Accurate segmentation of these lesions is crucial for radiological assessment and treatment planning.
Purpose of the Study:
- To address limitations in current brain metastasis segmentation, particularly for small lesions.
- To introduce an improved segmentation method and a novel evaluation metric for enhanced clinical utility.
Main Methods:
- Proposed DRAU-Net, incorporating a multi-branch weighted attention module and DResConv module for complete tumor boundary extraction.
- Introduced a multi-objective segmentation integrity metric for evaluating segmentation quality and target count, especially for small metastases.
Main Results:
- DRAU-Net achieved a Dice coefficient of 0.6858 and a multi-objective segmentation integrity metric of 0.5582 on the BraTS2023 dataset and clinical data.
- The proposed method demonstrated superior performance compared to existing techniques in segmenting metastatic tumors.
Conclusions:
- DRAU-Net offers a significant advancement in brain metastasis segmentation accuracy.
- The novel evaluation metric provides a more comprehensive assessment of segmentation performance for small, multiple lesions.
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