From Sparse to Precise: A Practical Editing Approach for Intracardiac Echocardiography Segmentation

• URL: http://arxiv.org/abs/2303.11041v2
• Date: Sun, 23 Jul 2023 10:54:21 GMT
• Title: From Sparse to Precise: A Practical Editing Approach for Intracardiac Echocardiography Segmentation
• Authors: Ahmed H. Shahin, Yan Zhuang, Noha El-Zehiry
• Abstract summary: We propose an interactive editing framework that allows users to edit segmentation output by drawing scribbles on a 2D frame. Our framework accommodates multiple edits to the segmentation output in a sequential manner without compromising previous edits.
• Score: 2.6910401398827117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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