3D Organ Shape Reconstruction from Topogram Images

TL;DR

This paper presents a method to predict 3D liver shapes directly from topogram images, reducing radiation exposure and simplifying the process compared to traditional CT-based techniques.

Contribution

The authors introduce a generative model that reconstructs 3D organ shapes from topogram images and incorporates user annotations for enhanced accuracy.

Findings

Accurate 3D liver shape reconstruction from topograms.

Improved volume estimation with user annotations.

Validated on 2129 CT scans.

Abstract

Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up. However, addressing this problem typically requires performing computed tomography (CT) scanning and complicated postprocessing of the resulting scans using slice-by-slice techniques. In this paper, we show that 3D organ shape can be automatically predicted directly from topogram images, which are easier to acquire and have limited exposure to radiation during acquisition, compared to CT scans. We evaluate our approach on the challenging task of predicting liver shape using a generative model. We also demonstrate that our method can be combined with user annotations, such as a 2D mask, for improved prediction accuracy. We show compelling results on 3D liver shape reconstruction and volume estimation on 2129 CT scans.