纵向CT扫描中用于COVID-19感染量化的交互式分割
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
AI总结：
针对纵向CT中COVID-19病灶分割问题，提出融合参考时间点、既往分割与用户涂鸦反馈的单网络交互式模型，实验证明其优于静态版本。
摘要：
在多个时间点上对COVID-19患者的CT扫描进行一致分割，对于准确评估疾病进展和治疗反应至关重要。现有的医学图像自动和交互式分割模型仅使用单个时间点（静态）的数据。然而，来自先前时间点的宝贵分割信息通常未被用于辅助患者随访扫描的分割。此外，全自动分割技术产生的结果往往需要进一步编辑才能用于临床。在本研究中，我们提出了一种新的交互式分割单一网络模型，该模型充分利用所有可用的既往信息来细化随访扫描的分割。在第一轮分割中，我们的模型将来自两个时间点（目标和参考）的医学图像3D体积作为拼接切片，并以额外的参考时间点分割作为指导来分割目标扫描。在随后的分割细化轮次中，用于纠正分割的涂鸦形式用户反馈以及目标的先前分割结果会被额外输入模型。这确保了来自先前细化轮次的分割信息得以保留。在我们内部的多类别纵向COVID-19数据集上的实验结果表明，所提出的模型优于其静态版本，并可辅助定位患者随访扫描中的COVID-19感染。
英文摘要：
Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic and interactive segmentation models for medical images only use data from a single time point (static). However, valuable segmentation information from previous time points is often not used to aid the segmentation of a patient's follow-up scans. Also, fully automatic segmentation techniques frequently produce results that would need further editing for clinical use. In this work, we propose a new single network model for interactive segmentation that fully utilizes all available past information to refine the segmentation of follow-up scans. In the first segmentation round, our model takes 3D volumes of medical images from two-time points (target and reference) as concatenated slices with the additional reference time point segmentation as a guide to segment the target scan. In subsequent segmentation refinement rounds, user feedback in the form of scribbles that correct the segmentation and the target's previous segmentation results are additionally fed into the model. This ensures that the segmentation information from previous refinement rounds is retained. Experimental results on our in-house multiclass longitudinal COVID-19 dataset show that the proposed model outperforms its static version and can assist in localizing COVID-19 infections in patient's follow-up scans.
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