Localization and Identification of Lumbar Intervertebral Discs on Spine MR Images with Faster RCNN Based Shortest Path Algorithm

Abstract

Automatic detection and identification of the intervertebral discs on the spine MR images is a challenging task due to similarity of the discs on the same image, size and shape differences between subjects, and poor resolution. Many deep learning-based methods have been proposed recently to achieve automated detection and identification of human intervertebral discs. However, since there is usually only a small amount of labeled vertebral images available, employing an end-to-end deep learning system is not easily achievable. In this paper, we use a multi-stage deep learning system to detect and identify human lumbar discs from MRI data. We first use a Faster Region based Convolutional Neural Network (FRCNN) method to detect candidate disc positions. Each candidate from the FRCNN becomes a node in a weighted graph structure. The edge weights between the nodes are calculated using the FRCNN scores and the scores from a Binary Classifier Network (BCN) that tests compatibility of the nodes of the edge. A novel application of Dijkstra’s shortest path algorithm in this network produces both localizations and identifications of the lumbar discs in a globally optimal manner. Experiments on our dataset of 80 MRI scans from 80 patients achieved very promising results as they exceeded the state of the art alternatives on similar datasets.