A fully automated level-set based segmentation method of thoracic and lumbar vertebral bodies in computed tomography images

Abstract

Spine is a structure commonly involved in several diseases. Identification and segmentation of the vertebral structures are of relevance to many medical applications related to the spine such as diagnosis, therapy or surgical intervention. However, the development of automatic and reliable methods are an unmet need. This work presents a fully automatic segmentation method of thoracic and lumbar vertebral bodies from Computed Tomography images. The procedure can be divided into four main stages: firstly, seed points were detected in the spinal canal in order to generate initial contours in the segmentation process, automating the whole process. Secondly, a processing step is performed to improve image quality. Third step was to carry out the segmentation using the Selective Binary Gaussian Filtering Regularized Level Set method and, finally, two morphological operations were applied in order to refine the segmentation result. The method was tested in clinical data coming from 10 trauma patients. To evaluate the result the average value of the DICE coefficient was calculated, obtaining a 90.86 +/- 1.87 % in the whole spine (thoracic and lumbar regions), a 86.08 +/- 1.73 % in the thoracic region and a 95,61 +/- 2,25 % in the lumbar region. The results are highly competitive when compared to the results obtained in previous methods, especially for the lumbar region.