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.

翻译：在心房颤动患者的导管消融术中，准确且安全地分割心腔内超声（ICE）成像中的心脏结构至关重要。已有研究提出利用ICE换能器的三维几何信息，将二维帧放置在三维网格中构建稀疏ICE体积，从而支持三维分割模型的训练。然而，由于ICE数据的稀疏采样、帧不对齐以及心脏运动，这些模型生成的三维掩膜可能不准确，并可能导致严重的临床并发症。为解决这一问题，我们提出了一种交互式编辑框架，允许用户通过在二维帧上绘制涂鸦来编辑分割输出。用户交互被映射到三维网格中，并用于执行编辑步骤，修改交互附近的分割结果，同时保持远离交互区域的原始分割不变。此外，我们的框架支持对分割输出进行多次顺序编辑，且不影响之前的编辑结果。本文提出了一种专为编辑设计的新型损失函数和评估指标。交叉验证和测试结果表明，与标准损失函数和训练策略相比，我们提出的损失函数在分割质量和遵循用户输入方面表现更优。同时，我们通过定量和定性分析证明，使用我们的方法时，后续编辑不会损害之前的编辑结果，这与使用标准分割损失函数的情况形成对比。总体而言，我们的方法提高了分割准确性，同时避免了用户交互区域外的不必要变化，且不损害已编辑区域的质量，从而改善了患者的治疗效果。