Keywords: Segmentation, Analysis/Processing
Motivation: Image segmentation holds significant importance in medical image analyses. Nevertheless, accurately segmenting images presents challenges, particularly due to data imbalances that are often encountered in multi-targets segmentation.
Goal(s): We aim to develop a model-independent loss function to enhance the multi-targets segmentation of medical images .
Approach: We develop a loss function, which integrates a contour-weighted cross-entropy loss with a separable dice loss. Moreover, we design a partial decoder attention network, named PDANet, to refine segmentation performance.
Results: Results on the BraTS dataset reveal that our loss function surpassed other existing methods, improving segmentation accuracy in widely used models, with our approach achieving superior results.
Impact: We developed a contour-weighted loss function to address the problem of data imbalance in medical image segmentation. Our approach is model-independent, allowing it to integrate seamlessly with any segmentation network, thereby improving segmentation performance across different models.
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