提出双掩码自编码器，提升肺部CT病灶分割的多尺度特征学习能力。
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT Segmentation
- 引入强度与空间双重掩码，增强模型对组织特征和边界细节的捕捉
- 在2D肺炎与纵隔肿瘤分割中，Dice达90.10%，性能稳定
- 适合医学图像分割研究者，尤其关注小病灶与边界精度的任务
在医学图像分割领域，病灶特征模糊、边界不清晰及多尺度特性长期存在挑战。本文提出改进方法Intensity-Spatial Dual Masked AutoEncoder（ISD-MAE）。基于组织对比半掩码自编码器，引入掩码自编码器分支，对胸部CT图像进行强度掩码与空间掩码操作，实现多尺度特征学习与分割。模型采用双分支结构结合对比学习，提升组织特征与边界细节的学习能力。在多个2D与3D数据集上实验表明，ISD-MAE在2D肺炎与纵隔肿瘤分割任务中显著优于现有方法，例如在COVID19 LESION数据集上Dice分数达到90.10%，表现稳定。然而3D数据集上仍有提升空间。后续方向包括优化损失函数、使用增强的3D卷积模块、多视角数据处理。代码已开源：https://github.com/prowontheus/ISD-MAE。
原文摘要 · Abstract (English)
In the field of medical image segmentation, challenges such as indistinct lesion features, ambiguous boundaries,and multi-scale characteristics have long revailed. This paper proposes an improved method named Intensity-Spatial Dual Masked AutoEncoder (ISD-MAE). Based on the tissue-contrast semi-masked autoencoder, a Masked AutoEncoder (MAE) branch is introduced to perform intensity masking and spatial masking operations on chest CT images for multi-scale feature learning and segmentation tasks. The model utilizes a dual-branch structure and contrastive learning to enhance the ability to learn tissue features and boundary details. Experiments are conducted on multiple 2D and 3D datasets. The results show that ISD-MAE significantly outperforms other methods in 2D pneumonia and mediastinal tumor segmentation tasks. For example, the Dice score reaches 90.10% on the COVID19 LESION dataset, and the performance is relatively stable. However, there is still room for improvement on 3D datasets. In response to this, improvement directions are proposed, including optimizing the loss function, using enhanced 3D convolution blocks, and processing datasets from multiple perspectives.Our code is available at:https://github.com/prowontheus/ISD-MAE.
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