TP-UNet：面向医学图像分割的时间提示引导UNet
TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation
AI总结：
本文提出TP-UNet，通过时间提示和交叉注意力及语义对齐机制，将器官构建关系融入UNet，提升医学图像分割精度。
摘要：
医学图像分割技术的进步得益于深度学习技术的采用，尤其是基于UNet的方法，这些方法利用语义信息来提高分割精度。然而，当前基于UNet的医学图像分割方法忽视了扫描图像中器官的顺序。此外，UNet固有的网络结构并不直接提供整合时间信息的能力。为了高效整合时间信息，我们提出了TP-UNet，它利用包含器官构建关系的时间提示来引导分割UNet模型。具体而言，我们的框架以基于无监督对比学习的交叉注意力和语义对齐为特色，以有效结合时间提示和图像特征。在两个医学图像分割数据集上的广泛评估证明了TP-UNet的最先进性能。我们的实现将在接收后开源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
