Spine centerline extraction and efficient spine reading of MRI and CT data.

Radiological assessment of the spine is performed regularly in the context of orthopedics, neurology, oncology, and trauma management. Due to the extension and curved geometry of the spinal column, reading is time-consuming and requires substantial user interaction to navigate through the data during inspection. In this paper a spine geometry guided viewing approach is proposed facilitating reading by reducing the degrees of freedom to be manipulated during inspection of the data. The method is using the spine centerline as a representation of the spine geometry. We assume that renderings most useful for reading are those that can be locally defined based on a rotation and translation relative to the spine centerline. The resulting renderings conserve locally the relation to the spine and lead to curved planar reformats that can be adjusted using a small set of parameters to minimize user interaction. The spine centerline is extracted by an automated image to image foveal fully convolutional neural network (FFCN) based approach. The network consists of three parallel convolutional pathways working on different levels of resolution and processed fields of view. The outputs of the parallel pathways are combined by a subsequent feature integration pathway to yield the (final) centerline probability map, which is converted into a set of spine centerline points. The network has been trained separately using two data set types, one comprising a mixture of T1 and T2 weighted spine MR images and one using CT image data. We achieve an average centerline position error of 1.7 mm for MR and 0.9 mm for CT and a DICE coefficient of 0.84 for MR and 0.95 for CT. Based on the thus obtained centerline viewing and multi-planar reformatting can be easily facilitated.