Automatic Lumbar Spinal CT Image Segmentation With a Dual Densely Connected U-Net

He Tang,Xin Li,Xiaobing Pei,Shilong Huang,Chao Liu

doi:10.1109/access.2020.2993867

He Tang, Xin Li + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.2993867

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 12	License type: CC BY 4.0

Affiliation: Huazhong University of Science and Technology

Abstract

The clinical treatment of degenerative and developmental lumbar spinal stenosis (LSS) is different. Computed tomography (CT) is helpful in distinguishing degenerative and developmental LSS due to its advantage in imaging osseous and calcified tissues. However, boundaries of the vertebral body, spinal canal and dural sac have low contrast and are hard to identify in a CT image, so the diagnosis depends heavily on the knowledge of expert surgeons and radiologists. In this paper, we develop an automatic lumbar spinal CT image segmentation method to assist LSS diagnosis. The main contributions of this paper are as follows: 1) a new lumbar spinal CT image dataset is constructed that contains 2393 axial CT images collected from 279 patients, with the ground truth of pixel-level segmentation labels; 2) a dual densely connected U-shaped neural network (DDU-Net) is used to segment the spinal canal, dural sac and vertebral body in an end-to-end manner; 3) DDU-Net is capable of segmenting tissues with large scale-variant, inconspicuous edges (e.g., spinal canal) and extremely small size (e.g., dural sac); and 4) DDU-Net is practical, requiring no image preprocessing such as contrast enhancement, registration and denoising, and the running time reaches 12 FPS. In the experiment, we achieved state-of-the-art performance on the lumbar spinal image segmentation task. We expect that the technique will increase both radiology workflow efficiency and the perceived value of radiology reports for referring clinicians and patients.

Highlights

Lumbar spinal stenosis (LSS) is one of the most common diseases encountered in spinal surgery practice
We provide a sufficiently labeled lumbar spinal computed tomography (CT) image dataset; the areas of the spinal canal, dural sac and vertebral body are labeled in pixel-level
Abbati et al [10] introduced magnetic resonance imaging (MRI)-based surgical planning for lumbar spinal stenosis, developing an automated algorithm to localize the stenosis causing the patient’s symptoms from the MR image; before training the network, the authors manually cropped the original images to obtain the region of interest and trained the network with both labeled and unlabeled images, and the results demonstrated promising performance

Summary

Introduction

Lumbar spinal stenosis (LSS) is one of the most common diseases encountered in spinal surgery practice. Diagnosis of LSS is usually made under the guidance of medical imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). More previous studies prefer MRI because it is safer and does not involve any radiation. The pathogenesis of degenerative LSS and developmental LSS differ [1]. Degeneration of the lumbar intervertebral disc, hypertrophy of the articular process and the calcification of ligamentum flavum are the main causes of degenerative LSS; treatment for patients is usually lumbar decompression. Developmental LSS is usually due to vertebral laminae osseous stenosis, and the corresponding treatment is usually laminectomy.

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