Fully automatic cervical vertebrae segmentation framework for X-ray images

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved

Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338

Lumbar Spine Location in Fluoroscopic Images by Evidence Gathering

Low back pain (LBP) is a very common problem and lumbar segmental instability is one of the causes. It is important to investigate lumbar spine movement in order to understand instability better and as an aid to diagnosis. Digital videofluoroscopy provides a method of quantifying the motion of individual vertebrae, but due to the relatively poor image quality, it is difficult and time consuming to locate landmarks manually, from which the kinematics can be calculated. Some semi-automatic approaches have already been developed but these are still time consuming and require some manual interaction. In this paper we apply the Hough transform (HT) to locate the lumbar spinal segments automatically. The HT is a powerful tool in computer vision and it has good performance in noise and partial occlusion. A recent arbitrary shape representation avoids problems inherent with tabular representations in the generalised HT (GHT) by describing shapes using a continuous formulation. The target shape is described by a set of Fourier descriptors, which vote in an accumulator space from which the object parameters of translation (including the x and y direction), rotation and scale can be determined. At present, this algorithm has been applied to the images of lumbar spine, and has been shown to provide satisfactory results. Further work will concentrate on reducing the computational time for real-time application, and on approaches to refine information at the apices, given initialisation by the new HT method

Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform

Low back pain is a very common problem in the industrialised countries and its associated cost is enormous. Diagnosis of the underlying causes can be extremely difficult. Many studies have focused on mechanical disorders of the spine. Digital videofluoroscopy (DVF) was widely used to obtain images for motion studies. This can provide motion sequences of the lumbar spine, but the images obtained often suffer due to noise, exacerbated by the very low radiation dosage. Thus determining vertebrae position within the image sequence presents a considerable challenge. In this paper, we show how our new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm as without it the extraction of moving multiple vertebrae is computationally daunting. Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders

Hough Forest-based Corner Detection for Cervical Spine Radiographs

The cervical spine (neck region) is highly sensitive to trauma related injuries, which must be analysed carefully by emergency physicians. In this work, we propose a Hough Forest-based corner detection method for cervical spine radiographs, as a first step towards a computer-aided diagnostic tool. We propose a novel patch-based model based on two-stage supervised learning (classification and regression) to estimate the corners of cervical vertebral bodies. Our method is evaluated using 106 cervical x-ray images consisting of 530 vertebrae and 2120 corners, which have been demarcated manually by an expert radiographer. The results show promising performance of the proposed algorithm, with a lowest median error of 1.98 m