  Delineation of anatomical structures is often the first step of many medical
image analysis workflows. While convolutional neural networks achieve high
performance, these do not incorporate anatomical shape information. We
introduce a novel segmentation algorithm that uses Deep Conditional Shape
models (DCSMs) as a core component. Using deep implicit shape representations,
the algorithm learns a modality-agnostic shape model that can generate the
signed distance functions for any anatomy of interest. To fit the generated
shape to the image, the shape model is conditioned on anatomic landmarks that
can be automatically detected or provided by the user. Finally, we add a
modality-dependent, lightweight refinement network to capture any fine details
not represented by the implicit function. The proposed DCSM framework is
evaluated on the problem of cardiac left ventricle (LV) segmentation from
multiple 3D modalities (contrast-enhanced CT, non-contrasted CT, 3D
echocardiography-3DE). We demonstrate that the automatic DCSM outperforms the
baseline for non-contrasted CT without the local refinement, and with the
refinement for contrasted CT and 3DE, especially with significant improvement
in the Hausdorff distance. The semi-automatic DCSM with user-input landmarks,
while only trained on contrasted CT, achieves greater than 92% Dice for all
modalities. Both automatic DCSM with refinement and semi-automatic DCSM achieve
equivalent or better performance compared to inter-user variability for these
modalities.
