自动分割鼻窦结构，量化炎症指标，提升慢性鼻窦炎评估客观性
PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure Analysis Tool for MRI
- 基于T1 MRI自动分割16个鼻窦结构的空气与软组织体积
- 空气区平均强度显著低于软组织，分离效果接近完美
- 可计算林德-麦克凯评分等临床指标，适合耳鼻喉科研究者
慢性鼻窦炎（CRS）影响5%-12%的人群，常因临床评估主观而难以诊断。本文提出PARASIDE，一种自动分割上颌窦、额窦、蝶窦和筛窦在T1 MRI中空气与软组织体积的工具。通过该分割，可量化此前仅能手动主观判断的特征关系。实验显示，空气结构平均强度始终低于软组织，具有近乎完美的可分性；健康人群软组织体积和强度均更低。本系统是首个实现16个鼻窦结构全自动化分割的工具，支持计算林德-麦克凯评分等医学相关特征。
原文摘要 · Abstract (English)
Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often difficult to assess due to its subjective nature in clinical evaluation. We introduce PARASIDE, an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenodalis and ethmoidalis in T1 MRI. By utilizing that segmentation, we can quantify feature relations that have been observed only manually and subjectively before. We performed an exemplary study and showed both volume and intensity relations between structures and radiology reports. While the soft tissue segmentation is good, the automated annotations of the air volumes are excellent. The average intensity over air structures are consistently below those of the soft tissues, close to perfect separability. Healthy subjects exhibit lower soft tissue volumes and lower intensities. Our developed system is the first automated whole nasal segmentation of 16 structures, and capable of calculating medical relevant features such as the Lund-Mackay score.
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