A Knowledge-Based Approach to Soft Tissue Reconstruction of the Cervical Spine

• Published in: IEEE transactions on medical imaging Vol. 28; no. 4; pp. 494 - 507
• Main Authors: Seifert, S., Wachter, I., Schmelzle, G., Dillmann, R.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anatomical structure; Automatic segmentation; Biological tissues; cervical soft tissue; Cervical Vertebrae - anatomy & histology; Cluster Analysis; generalized segmentation workflow; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Intervertebral Disc - anatomy & histology; Magnetic Resonance Imaging; muscle reconstruction; Neck - anatomy & histology; Neck Muscles - anatomy & histology; Object recognition; Pattern Recognition, Automated - methods; Reconstruction algorithms; Reproducibility of Results; Spinal cord; Spine; Surface reconstruction; Surgery; Time Factors; Tomography
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2008.2004659

For surgical planning in spine surgery, the segmentation of anatomical structures is a prerequisite. Past efforts focussed on the segmentation of vertebrae from tomographic data, but soft tissue structures have, for the most part, been neglected. Only sparse research work has been done for the spinal cord and the trachea. However, as far as the author is aware, there is no work on segmenting intervertebral discs. Therefore, a totally automatic reconstruction algorithm for the most relevant cervical structures is presented. It is implemented as a straightforward process, using anatomical knowledge which is, in concept, transferrable to other tissues of the human body. No seed points are required since the discs, as initial landmarks, are located via an object recognition approach. The spinal musculature is reconstructed by surface analysis on already segmented vertebrae, thus it can be taken into account in a biomechanical simulation. The segmentation results of our approach showed 91% accordance with expert segmentations and the computation time is less than 1 min on a standard PC. Since the presented system follows some general concepts this approach may also be considered as a step towards full body segmentation of the human.