Featured by a bottleneck structure, autoencoder (AE) and its variants have been largely applied in various medical image analysis tasks, such as segmentation, reconstruction and de-noising. Despite of their promising performances in aforementioned tasks, in this paper, we claim that AE models are not applicable to single image super-resolution (SISR) for 3D CT data. Our hypothesis is that the bottleneck architecture that resizes feature maps in AE models degrades the details of input images, thus can sabotage the performance of super-resolution. Although U-Net proposed skip connections that merge information from different levels, we claim that the degrading impact of feature resizing operations could hardly be removed by skip connections. By conducting large-scale ablation experiments and comparing the performance between models with and without the bottleneck design on a public CT lung dataset , we have discovered that AE models, including U-Net, have failed to achieve a compatible SISR result ($p<0.05$ by Student's t-test) compared to the baseline model. Our work is the first comparative study investigating the suitability of AE architecture for 3D CT SISR tasks and brings a rationale for researchers to re-think the choice of model architectures especially for 3D CT SISR tasks. The full implementation and trained models can be found at: https://github.com/Roldbach/Autoencoder-3D-CT-SISR

翻译：以其瓶颈结构为特征，自编码器（AE）及其变体已被广泛应用于各种医学图像分析任务，例如分割、重建和去噪。尽管在上述任务中表现优异，但本文提出，AE模型不适用于三维CT数据的单图像超分辨率（SISR）。我们的假设是，AE模型中用于调整特征图尺寸的瓶颈架构会降低输入图像的细节，从而损害超分辨率的性能。尽管U-Net提出了从不同层级融合信息的跳跃连接，但我们认为特征重采样操作的退化影响难以被跳跃连接消除。通过在公开CT肺部数据集上进行大规模消融实验并比较带瓶颈设计与不带瓶颈设计的模型性能，我们发现包括U-Net在内的AE模型未能达到与基线模型相当的SISR结果（学生t检验，p<0.05）。本研究首次针对AE架构在三维CT SISR任务中的适用性进行比较分析，为研究者重新思考模型架构选择（尤其针对三维CT SISR任务）提供了理论依据。完整实现及训练模型可在以下网址获取：https://github.com/Roldbach/Autoencoder-3D-CT-SISR