用时间提示引导的UNet模型提升医学图像分割精度
TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation
- 引入器官构建关系的时间提示，增强模型对解剖顺序的理解
- 在两个数据集上达到当前最佳性能，有效融合时序信息
- 适合需要精确解剖结构分割的医学影像研究者使用
医学图像分割技术的发展得益于深度学习，尤其是基于UNet的方法，其通过利用语义信息提升了分割准确性。然而，现有基于UNet的分割方法忽略了扫描图像中器官的排列顺序，且原始网络结构缺乏直接整合时间信息的能力。为高效融合时间信息，本文提出TP-UNet，利用包含器官构建关系的时间提示引导分割模型。该框架采用基于无监督对比学习的交叉注意力与语义对齐机制，有效结合时间提示与图像特征。在两个医学图像分割数据集上的广泛评估表明，TP-UNet实现了当前最优性能。代码将在论文接受后开源。
原文摘要 · Abstract (English)
The advancement of medical image segmentation techniques has been propelled by the adoption of deep learning techniques, particularly UNet-based approaches, which exploit semantic information to improve the accuracy of segmentations. However, the order of organs in scanned images has been disregarded by current medical image segmentation approaches based on UNet. Furthermore, the inherent network structure of UNet does not provide direct capabilities for integrating temporal information. To efficiently integrate temporal information, we propose TP-UNet that utilizes temporal prompts, encompassing organ-construction relationships, to guide the segmentation UNet model. Specifically, our framework is featured with cross-attention and semantic alignment based on unsupervised contrastive learning to combine temporal prompts and image features effectively. Extensive evaluations on two medical image segmentation datasets demonstrate the state-of-the-art performance of TP-UNet. Our implementation will be open-sourced after acceptance.
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