3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm

3D Ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. However, the coronal images from different depths of a 3D ultrasound image have different imaging definitions. So there is a need to select the coronal image that would give the best image definition. Also, manual selection of coronal images is time-consuming and limited to the discretion and capability of the assessor. Therefore, in this paper, we have developed a convolution learning-to-rank algorithm to select the best ultrasound images automatically using raw ultrasound images. The ranking is done based on the curve angle of the spinal cord. Firstly, we approached the image selection problem as a ranking problem; ranked based on probability of an image to be a good image. Here, we use the RankNet, a pairwise learning-to-rank method, to rank the images automatically. Secondly, we replaced the backbone of the RankNet, which is the traditional artificial neural network (ANN), with convolution neural network (CNN) to improve the feature extracting ability for the successive iterations. The experimental result shows that the proposed convolutional RankNet achieves the perfect accuracy of 100% while conventional DenseNet achieved 35% only. This proves that the convolutional RankNet is more suitable to highlight the best quality of ultrasound image from multiple mediocre ones.

I. Introduction

Scoliosis is a deformation of spinal cord to usually "S" or "C" shape and a degree of curvature occurs in the coronal plane. Since, the prime victims of this disease are adolescents, it is very important to screen schoolchildren. According to current study [1], about 0.47-5.2% of total population is affected by scoliosis. The traditional diagnose process of scoliosis is detection of spine curvature angle using Cobb’s method on standing radio-graph. Traditional techniques such as MRI and CT Scan uses radiation and their frequent exposure may causes cancer.