CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models

TL;DR

This paper introduces a novel deep learning framework for recovering CT films from photographs affected by geometric deformation and illumination variation, utilizing a large simulated dataset and disentangling techniques.

Contribution

It is the first to address CT film recovery, creating a large-scale simulated dataset and proposing a deep model to disentangle deformation and lighting effects.

Findings

Our method outperforms previous approaches on simulated and real images.

The large-scale CTFilm20K dataset enables robust training and evaluation.

Disentangling geometric and illumination factors improves recovery accuracy.

Abstract

While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable medium for the purposes of self-storage and secondary consultation. Also, with the ubiquitousness of mobile phone cameras, it is quite common to take pictures of the CT films, which unfortunately suffer from geometric deformation and illumination variation. In this work, we study the problem of recovering a CT film, which marks the first attempt in the literature, to the best of our knowledge. We start with building a large-scale head CT film database CTFilm20K, consisting of approximately 20,000 pictures, using the widely used computer graphics software Blender. We also record all accompanying information related to the geometric deformation (such as 3D coordinate, depth, normal, and UV maps) and illumination variation (such as albedo map). Then we propose a deep framework to disentangle geometric deformation and illumination variation using the multiple maps extracted from the CT films to collaboratively guide the recovery process. Extensive experiments on simulated and real images demonstrate the superiority of our approach over the previous approaches. We plan to open source the simulated images and deep models for promoting the research on CT film recovery (https://anonymous.4open.science/r/e6b1f6e3-9b36-423f-a225-55b7d0b55523/).