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

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Medical Image Understanding and Analysis (MIUA 2020)


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.

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We would like to thank Dr. Ayse Betul Oktay for providing the dataset and also TUBITAK-BILGEM Cloud Computing and Big Data Laboratory (B3LAB) for allowing us to use their GPU servers.

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Correspondence to Merve Zeybel .

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Zeybel, M., Akgul, Y.S. (2020). Localization and Identification of Lumbar Intervertebral Discs on Spine MR Images with Faster RCNN Based Shortest Path Algorithm. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham.

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