科研之友
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摘要
Lumbar spondylolisthesis is one of the most common spinal diseases. It is caused by the anterior shift of a lumbar vertebrae relative to subjacent vertebrae. In current clinical practices, staging of spondylolisthesis is often conducted in a qualitative way. Although meyerding grading opens the door to stage spondylolisthesis in a more quantitative way, it relies on the manual measurement, which is time consuming and irreproducible. Thus, an automatic measurement algorithm becomes desirable for spondylolisthesis diagnosis and staging. However, there are two challenges. 1) Accurate detection of the most anterior and posterior points on the superior and inferior surfaces of each lumbar vertebrae. Due to the small size of the vertebrae, slight errors of detection may lead to significant measurement errors, hence, wrong disease stages. 2) Automatic localize and label each lumbar vertebrae is required to provide the semantic meaning of the measurement. It is difficult since different lumbar vertebraes have high similarity of both shape and image appearance. To resolve these challenges, a new auto measurement framework is proposed with two major contributions: First, a learning based spine labeling method that integrates both the image appearance and spine geometry information is designed to detect lumbar vertebrae. Second, a hierarchical method using both the population information from atlases and domain-specific information in the target image is proposed for most anterior and posterior points positioning. Validated on 258 CT spondylolisthesis patients, our method shows very similar results to manual measurements by radiologists and significantly increases the measurement efficiency.
- 出版日期2016-7
- 单位中国人民解放军第306医院
