3D Organ Shape Reconstruction from Topogram Images
This addresses the need for efficient organ delineation in medical imaging for disease assessment and treatment planning, offering a more accessible alternative to CT-based methods.
The paper tackles the problem of automatically reconstructing 3D organ shapes, specifically the liver, from topogram images, which are easier to acquire and have lower radiation exposure than CT scans, achieving results on 2129 CT scans.
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.