Automatic vertebrae detection and labeling in sagittal magnetic resonance images

Sammanfattning: Radiologists are often plagued by limited time for completing their work, with an ever increasing workload. A picture archiving and communication system (PACS) is a platform for daily image reviewing that improves their work environment, and on that platform for example spinal MR images can be reviewed. When reviewing spinal images a radiologist wants vertebrae labels, and in Sectra's PACS platform there is a good opportunity for implementing an automatic method for spinal labeling. In this thesis a method for performing automatic spinal labeling, called a vertebrae classifier, is presented. This method should remove the need for radiologists to perform manual spine labeling, and could be implemented in Sectra's PACS software to improve radiologists overall work experience.Spine labeling is the process of marking vertebrae centres with a name on a spinal image. The method proposed in this thesis for performing that process was developed using a machine learning approach for vertebrae detection in sagittal MR images. The developed classifier works for both the lumbar and the cervical spine, but it is optimized for the lumbar spine. During the development three different methods for the purpose of vertebrae detection were evaluated. Detection is done on multiple sagittal slices. The output from the detection is then labeled using a pictorial structure based algorithm which uses a trained model of the spine to correctly assess correct labeling. The suggested method achieves 99.6% recall and 99.9% precision for the lumbar spine. The cervical spine achieves slightly worse performance, with 98.1% for both recall and precision. This result was achieved by training the proposed method on 43 images and validated with 89 images for the lumbar spine. The cervical spine was validated using 26 images. These results are promising, especially for the lumbar spine. However, further evaluation is needed to test the method in a clinical setting.