ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist
ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist
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DOI：
10.1007/s00586-017-4956-3
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发表时间：
2017-05-01
影响因子：
2.8
通讯作者：
McCall, Iain
中科院分区：
文献类型：
--
作者：
Jamaludin, Amir;Lootus, Meelis;McCall, Iain
腰椎磁共振影像（MRI）放射学特征自动化的研究。通过MRIs对腰椎间盘和椎体进行自动分级。MR成像是研究腰痛（LBP）最常用的成像技术。基于MRI的各种退化特征通常被记录和分级，例如，椎间盘Modic改变和Pfirrmann分级。一致的评分和分级对于开发强大的临床系统和研究非常重要。自动化促进了这种一致性，并大大减少了放射学分析的时间，从而降低了费用。来自2009名患者的12，018个椎间盘由放射科医生分级，然后用于训练：（1）在给定扫描中检测和标记椎骨和椎间盘的系统，以及（2）预测几个放射学分级的卷积神经网络（CNN）模型。该模型的性能，在类平均准确度方面，与放射科医生的观察者内类平均准确度进行了比较。检测系统在椎间盘检测和标记方面达到了95.6%的准确率。该模型能够产生与放射科医生一致的多个病理分级的预测。该模型识别出对退化评分贡献最大的体素“证据热点”。放射学分级的自动化现在与人类的表现不相上下。该系统在分级的客观性和分析的速度方面可以有益于辅助临床诊断。它还可以引起放射科医生对退化区域的注意。这种客观性和快速性是研究MRI与大型队列中背痛临床诊断之间关系的重要基石。证据等级：3级。
Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine.To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs.MR imaging is the most common imaging technique used in investigating low back pain (LBP). Various features of degradation, based on MRIs, are commonly recorded and graded, e.g., Modic change and Pfirrmann grading of intervertebral discs. Consistent scoring and grading is important for developing robust clinical systems and research. Automation facilitates this consistency and reduces the time of radiological analysis considerably and hence the expense.12,018 intervertebral discs, from 2009 patients, were graded by a radiologist and were then used to train: (1) a system to detect and label vertebrae and discs in a given scan, and (2) a convolutional neural network (CNN) model that predicts several radiological gradings. The performance of the model, in terms of class average accuracy, was compared with the intra-observer class average accuracy of the radiologist.The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model is able to produce predictions of multiple pathological gradings that consistently matched those of the radiologist. The model identifies 'Evidence Hotspots' that are the voxels that most contribute to the degradation scores.Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts.Level of Evidence: Level 3.