High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3D images remains a challenge due to their high spatial resolution and dimensionality in contrast to currently limited GPU memory. Therefore, most existing 3D image segmentation methods use patch-based models, which have low inference efficiency and ignore global contextual information. To address these problems, we propose a super-resolution (SR) based patch-free 3D image segmentation framework that can realize HR segmentation from a global-wise low-resolution (LR) input. The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input. To furthermore balance the information loss with the LR input, we propose a High-Frequency Guidance Module (HGM), and design an efficient selective cropping algorithm to crop an HR patch from the original image as restoration guidance for it. In addition, we also propose a Task-Fusion Module (TFM) to exploit the inter connections between segmentation and SR task, realizing joint optimization of the two tasks. When predicting, only the main segmentation task is needed, while other modules can be removed for acceleration. The experimental results on two different datasets show that our framework has a four times higher inference speed compared to traditional patch-based methods, while its performance also surpasses other patch-based and patch-free models.

翻译：高分辨率三维图像（如磁共振成像MRI和计算机断层扫描CT等医学图像）在当前被广泛应用。然而，由于这些三维图像具有高空间分辨率和维度，与当前GPU内存限制形成对比，其分割仍然是一个挑战。因此，现有大多数三维图像分割方法采用基于块的模型，此类模型推理效率低且忽略全局上下文信息。为解决这些问题，我们提出一种基于超分辨率（SR）的无块三维图像分割框架，可从全局尺度的低分辨率（LR）输入实现高分辨率分割。该框架包含两个子任务：语义分割为主任务，超分辨率为辅助任务，协助重建LR输入中的高频信息。为平衡LR输入带来的信息损失，我们提出高频引导模块（HGM），并设计高效的选择性裁剪算法，从原始图像中裁剪高分辨率块作为其重建引导。此外，我们还提出任务融合模块（TFM），以挖掘分割与超分辨率任务间的内在联系，实现两任务的联合优化。在预测阶段，仅需保留主分割任务，其余模块可被移除以加速推理。在两个不同数据集上的实验结果表明，与传统的基于块的方法相比，本框架的推理速度提升四倍，同时性能也优于其他基于块和无块模型。