ExSwin-Unet: An Unbalanced Weighted Unet with Shifted Window and External Attentions for Fetal Brain MRI Image Segmentation

计算机科学 分割 编码器 人工智能 模式识别(心理学) 变压器 图像分割 机器学习 电压 操作系统 物理 量子力学
作者
Yufei Wen,Chongxin Liang,Jingyin Lin,Huisi Wu,Jing Qin
出处
期刊:Lecture Notes in Computer Science 卷期号:: 340-354 被引量:1
标识
DOI:10.1007/978-3-031-25066-8_18
摘要

AbstractAccurate fetal brain MRI image segmentation is essential for fetal disease diagnosis and treatment. While manual segmentation is laborious, time-consuming, and error-prone, automated segmentation is a challenging task owing to (1) the variations in shape and size of brain structures among patients, (2) the subtle changes caused by congenital diseases, and (3) the complicated anatomy of brain. It is critical to effectively capture the long-range dependencies and correlations among training samples to yield satisfactory results. Recently, some transformer-based models have been proposed and achieved good performance in segmentation tasks. However, the self-attention blocks embedded in transformers often neglect the latent relationships among different samples. Model may have biased results due to the unbalanced data distribution in the training dataset. We propose a novel unbalanced weighted Unet equipped with a new ExSwin transformer block to comprehensively address the above concerns by effectively capturing long-range dependencies and correlations among different samples. We design a deeper encoder to facilitate features extracting and preserving more semantic details. In addition, an adaptive weight adjusting method is implemented to dynamically adjust the loss weight of different classes to optimize learning direction and extract more features from under-learning classes. Extensive experiments on a FeTA dataset demonstrate the effectiveness of our model, achieving better results than state-of-the-art approaches.KeywordsFetal brain MRI imagesTransformerMedical image segmentation
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