Is Autoencoder Truly Applicable for 3D CT Super-Resolution?

TL;DR

This study critically evaluates autoencoder architectures for 3D CT image super-resolution, revealing that their bottleneck design degrades image details and hampers performance compared to baseline models.

Contribution

First comprehensive comparison showing autoencoders, including U-Net, are unsuitable for 3D CT super-resolution due to bottleneck-induced detail loss.

Findings

Autoencoders underperform baseline models in 3D CT SISR ($p<0.05$).

Bottleneck architecture degrades image details, affecting super-resolution quality.

Skip connections do not compensate for feature resizing degradation.

Abstract

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