论文标题
整个脊柱MRI中的椎骨检测和标记的卷积方法
A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI
论文作者
论文摘要
我们提出了一种新型的卷积方法，用于在整个脊柱MRI中检测和鉴定椎骨。这涉及使用学到的矢量场将检测到椎骨角的分组分为单个椎体和卷积图像到图像的翻译，然后以横梁搜索以自吻合的方式将椎骨搜索到标记椎骨级别。该方法可以在不修改腰部，颈椎和胸腔范围内扫描一系列不同的MR序列的情况下应用。在充满挑战的整个脊柱扫描和匹配的临床数据集上，最终的系统可实现98.1％的检测率和96.5％的识别率，或者超过了先前系统在仅腰部扫描中的性能。最后，我们证明了这种方法的临床适用性，并在腰椎和整个脊柱MR扫描中使用它用于自动脊柱侧弯检测。
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner. The method can be applied without modification to lumbar, cervical and thoracic-only scans across a range of different MR sequences. The resulting system achieves 98.1% detection rate and 96.5% identification rate on a challenging clinical dataset of whole spine scans and matches or exceeds the performance of previous systems on lumbar-only scans. Finally, we demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
