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- W3217500219 abstract "Medical image segmentation based on deep learning technics has been more and more prevalent in recent years. The primary reasons lead to success of those methods are radical technics in improving deep learning model or global loss functions such as Cross Entropy (CE), Dice loss, … However, it is still a challenging problem because medical images are normally affected by complex noises, intensity inhomogeneity, or occlusion. To effectively solve those issues, utilizing local image information for segmentation is demonstrated as the potential way. Therefore, in this paper, we propose a new region-based loss functional based on level set method and extent to the case of multiphase segmentation. It allows our deep learning model to train end-to-end and also segment multiclass simultaneously with high accuracy instead of binary segmentation. Our proposed method is assessed on 2017 ACDC dataset and 2018 ISIC Challenge dataset. Experiments indicate that the proposed method outperforms the state-of-the-art methods in terms of Dice coefficient and Jaccard indexes. This highlights the efficiency of our approach in multiclass segmentation for medical images." @default.
- W3217500219 created "2021-12-06" @default.
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- W3217500219 date "2021-11-23" @default.
- W3217500219 modified "2023-10-17" @default.
- W3217500219 title "A Deep Learning-Based Approach with Image-Driven Active Contour Loss for Medical Image Segmentation" @default.
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- W3217500219 doi "https://doi.org/10.1007/978-981-16-5120-5_1" @default.
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