Author:
Cina Andrea,Bassani Tito,Panico Matteo,Luca Andrea,Masharawi Youssef,Brayda-Bruno Marco,Galbusera Fabio
Abstract
AbstractIn this work we propose to use Deep Learning to automatically calculate the coordinates of the vertebral corners in sagittal x-rays images of the thoracolumbar spine and, from those landmarks, to calculate relevant radiological parameters such as L1–L5 and L1–S1 lordosis and sacral slope. For this purpose, we used 10,193 images annotated with the landmarks coordinates as the ground truth. We realized a model that consists of 2 steps. In step 1, we trained 2 Convolutional Neural Networks to identify each vertebra in the image and calculate the landmarks coordinates respectively. In step 2, we refined the localization using cropped images of a single vertebra as input to another convolutional neural network and we used geometrical transformations to map the corners to the original image. For the localization tasks, we used a differentiable spatial to numerical transform (DSNT) as the top layer. We evaluated the model both qualitatively and quantitatively on a set of 195 test images. The median localization errors relative to the vertebrae dimensions were 1.98% and 1.68% for x and y coordinates respectively. All the predicted angles were highly correlated with the ground truth, despite non-negligible absolute median errors of 1.84°, 2.43° and 1.98° for L1–L5, L1–S1 and SS respectively. Our model is able to calculate with good accuracy the coordinates of the vertebral corners and has a large potential for improving the reliability and repeatability of measurements in clinical tasks.
Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. Cirillo, D. & Valencia, A. Big data analytics for personalized medicine. Curr. Opin. Biotechnol. 58, 161–167 (2019).
2. Le Huec, J. C., Charosky, S., Barrey, C., Rigal, J. & Aunoble, S. Sagittal imbalance cascade for simple degenerative spine and consequences: algorithm of decision for appropriate treatment. Eur. Spine J. 20(Suppl 5), 699–703 (2011).
3. Carman, D. L., Browne, R. H. & Birch, J. G. Measurement of scoliosis and kyphosis radiographs. Intraobserver and interobserver variation. J. Bone Joint Surg. Am. 72(3), 328–333 (1990).
4. Summers R.M. Deep learning and computer-aided diagnosis for medical image processing: a personal perspective. In Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer, Cham, 3–10 (2017).
5. Wu, H., Bailey, C., Rasoulinejad, P. & Li, S. Automated comprehensive adolescent idiopathic scoliosis assessment using MVC-Net. Med. Image Anal. 48, 1–11 (2018).
Cited by
27 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献