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Abstract #4303

Improving medical image segmentation using contour-weighted loss

Zhengyong Huang1,2, Ning Jiang1,2, and Yao Sui1,2
1National Institute of Health Data Science, Peking University, Beijing, China, 2Institute of Medical Technology, Peking University, Beijing, China

Synopsis

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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Keywords