TTT-Unet通过测试时训练提升分割精度，尤其擅长捕捉长距离依赖。
TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation
- 在U-Net中加入测试时训练层，动态调整参数以增强特征捕获能力。
- 在3个医学图像数据集上均超越现有CNN与Transformer模型，表现稳定领先。
- 适合需要高精度分割的医疗影像分析场景，尤其适用于复杂结构识别。
生物医学图像分割对疾病诊断与分析至关重要。然而，当前广泛使用的卷积神经网络（CNN）和变换器（Transformer）因CNN固有的局部性及Transformer计算复杂度高，难以有效捕捉长程依赖关系。为此，我们提出TTT-Unet，一种将测试时训练（TTT）层融入传统U-Net架构的新框架，用于生物医学图像分割。该方法在测试阶段动态调整模型参数，显著提升模型对局部与长程特征的建模能力。我们在多个医学影像数据集上进行了评估，涵盖3D腹部器官在CT与MR图像中的分割、内窥镜图像中器械分割以及显微图像中的细胞分割任务。结果表明，TTT-Unet在所有任务中持续优于现有的基于CNN与Transformer的先进分割模型。代码已公开于https://github.com/rongzhou7/TTT-Unet。
原文摘要 · Abstract (English)
Biomedical image segmentation is crucial for accurately diagnosing and analyzing various diseases. However, Convolutional Neural Networks (CNNs) and Transformers, the most commonly used architectures for this task, struggle to effectively capture long-range dependencies due to the inherent locality of CNNs and the computational complexity of Transformers. To address this limitation, we introduce TTT-Unet, a novel framework that integrates Test-Time Training (TTT) layers into the traditional U-Net architecture for biomedical image segmentation. TTT-Unet dynamically adjusts model parameters during the testing time, enhancing the model's ability to capture both local and long-range features. We evaluate TTT-Unet on multiple medical imaging datasets, including 3D abdominal organ segmentation in CT and MR images, instrument segmentation in endoscopy images, and cell segmentation in microscopy images. The results demonstrate that TTT-Unet consistently outperforms state-of-the-art CNN-based and Transformer-based segmentation models across all tasks. The code is available at https://github.com/rongzhou7/TTT-Unet.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。