  Accurate and safe catheter ablation procedures for patients with atrial
fibrillation require precise segmentation of cardiac structures in Intracardiac
Echocardiography (ICE) imaging. Prior studies have suggested methods that
employ 3D geometry information from the ICE transducer to create a sparse ICE
volume by placing 2D frames in a 3D grid, enabling training of 3D segmentation
models. However, the resulting 3D masks from these models can be inaccurate and
may lead to serious clinical complications due to the sparse sampling in ICE
data, frames misalignment, and cardiac motion. To address this issue, we
propose an interactive editing framework that allows users to edit segmentation
output by drawing scribbles on a 2D frame. The user interaction is mapped to
the 3D grid and utilized to execute an editing step that modifies the
segmentation in the vicinity of the interaction while preserving the previous
segmentation away from the interaction. Furthermore, our framework accommodates
multiple edits to the segmentation output in a sequential manner without
compromising previous edits. This paper presents a novel loss function and a
novel evaluation metric specifically designed for editing. Results from
cross-validation and testing indicate that our proposed loss function
outperforms standard losses and training strategies in terms of segmentation
quality and following user input. Additionally, we show quantitatively and
qualitatively that subsequent edits do not compromise previous edits when using
our method, as opposed to standard segmentation losses. Overall, our approach
enhances the accuracy of the segmentation while avoiding undesired changes away
from user interactions and without compromising the quality of previously
edited regions, leading to better patient outcomes.
