A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

Abstract

Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.

Keywords: Cobb angle measurement; deep learning; image enhancement; scoliosis.

Figure 1

Schematic of Cobb angle measurements: the Cobb angle is defined as the angle between the extension line of the upper end plate of the most inclined vertebral body in a curved segment and the extension line of the lower end plate of the most inclined vertebral body below.

Figure 2

Schematic of octa segmentation method proposed by Chockalingam, N. et al. [42].

Figure 3

The X-ray images processed by the GVF-snake model [48].

Figure 4

The structure of the improved U-Net [55]: Compared with the original U-net, Wang Z. [58] has made two modifications. First, in order to reduce the amount of calculation, modify conv (3 × 3) to conv (1 × 1) + conv (3 × 3) + conv (1 × 1). In order to reduce the overall parameters, conv (3 × 3) is replaced with conv (1 × 1) when dealing with the same dimension and dimension reduction calculation.

Figure 5

The framework of S²VR [64]: SVR is used to capture the angle and landmark output correlation structure matrix, improved to obtain S2VR. Sun, H. et al. [64] introduced manifold regularization and trained with kernel alignment method.

Figure 6

The structure of Boostnet [65]. The Boostnet consists of three parts: a convolutional layer for feature extraction, a boostlayer for removing outliers, and a multi-output layer for relieving the pressure of small datasets by capturing inter-landmark dependencies.

Figure 7

The structure of the MVC-net [67]. MVC-net consists of three parts: a convolutional layer for feature extraction (the convolutional layers are connected with X modules for feature joint learning); a spine landmark output layer and a Cobb angle output layer.

Figure 8

The architecture of RSN-U-net [97]: Similar to U-net, RSN-U-net is also composed of an encoder and decoder. The encoder section consists of four repeated encoder stacks. Each encoder stack contains two conv (3 × 3) convolutional layers and one max-pooling (2). Each decoder stack contains two conv (3 × 3) and one Upsampling (2).

Figure 9

Block diagram and the architectures of Faster-RCNN and Resnet [15]: Yang, J. et al. (a) The entire DLA workflow; (b) The architecture and workflow of Faster-RCNN; (c) The architecture of Resnet. [15]’s algorithm using convolutional layer to obtain a feature map of the back. The algorithm finds the ROI in the feature map, pools it, and classifies it with a classifier.