  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.
