发表机构
CQUniversity; Tsinghua Shenzhen International Graduate School, Tsinghua University（中央昆士兰大学; 清华大学深圳国际研究生院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对心血管导管造影图像多类别分割需求，提出双部分MLBNet架构，经 phantom 模型、合成主动脉及动物模型数据验证，可准确分割导丝、导管、血管及背景等类别，性能优异。
AI 中文摘要
导管图像分割模型需具备快速、准确且可解释的特性，现有研究多聚焦于二值分割，但近期出现了同时分割导管场景中多种结构的需求。本研究设计了一种双部分MLBNet架构，包含多侧编码器块和多头解码器分支，用于心血管导管场景的类别感知分割。编码器中的侧分支支持重复特征提取以学习多样的共享表示，多个解码器头用于引入类别偏斜分支，专门处理导管场景中不同的结构特性。为分析双部分MLBNet架构的性能，使用在 phantom 模型、合成人体模拟主动脉及动物模型中心血管导管检查过程中获取的多类别分割造影数据进行模型训练与评估。结果表明，双部分模型可有效将导丝、导管、血管及背景像素按所属类别进行高概率区分，所有模型均能以高总体准确率区分占主导的背景类别与前景结构。
英文摘要
Catheterisation image processing requires segmentation models that are fast, accurate and explainable. While most of the existing studies usually focus on binary segmentation, there is a recent demand for simultaneous segmentation of multiple structures found in catheterization scenes. In this study, a dual-part MLBNet architecture is designed with multi-lateral encoder blocks and multi-head decoder branches for class-aware segmentation in cardiovascular catheterization scenes. Lateral branches in the encoder enables repeated feature extraction to learn diverse shared representations, while multiple decoder heads are used to introduce class-skewed branches that specialize in different structural properties in catheterization scenes. To analyze the performances of the dual-part MLBNet architecture, several multi-class segmentation angiogram data obtained during cardiovascular catheterization in phantom models, synthetic human-simulated aorta, and animal model are used for model training and evaluation. Results obtained showed the dual-part models could effectively separate guidewire, catheter, vessels and background pixels to their classes of memberships with high probability. The results demonstrate that all models were able to distinguish the dominant background class from foreground structures with high overall accuracy.
