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.Comment: Version #2 uploaded in Jul 10, 202