PDF(Pubmed)
Abstract：
Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model\'s grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available.
摘要：
脊柱磁共振（MR）扫描是诊断许多疾病和病症的背痛原因的重要工具。然而,从这些扫描中解释临床有用的信息可能是具有挑战性的，耗时且难以在不同的放射科医师之间复制。在本文中,我们通过引入用于分析脊柱MR扫描的多级自动化管道来缓解这些问题。该管道首先检测并标记跨几个常用序列的椎体(例如T1w，T2w和STIR)和视野(例如腰椎，子宫颈,整个脊柱)。使用这些检测，然后对几种脊柱疾病进行自动诊断，包括T1w和T2w腰椎扫描的椎间盘退行性改变，和脊柱转移瘤，脊髓压缩和椎骨骨折。为了实现这一点，我们提出了一种新的椎骨检测和标记方法，使用矢量场将检测到的椎骨标志组合在一起，并进行语言建模启发的波束搜索以确定检测的相应级别。我们还采用了一种新的基于变压器的架构来执行放射分级，该分级结合了来自多个椎骨和序列的上下文，就像真正的放射科医生一样.在几个临床数据集上单独测试管道每个阶段的性能，每个由66到421个扫描。将输出与放射科专家的手动注释进行比较，在一系列扫描参数中展示准确的椎骨检测。同样，该模型对不同类型椎间盘退变和脊柱转移瘤检测的分级预测与放射科专家的分级预测非常吻合。为了帮助未来的研究，我们的代码和训练的模型是公开可用的。
