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Table 3 Summary of recent literature

From: Spinal vertebrae localization and analysis on disproportionality in curvature using radiography—a comprehensive review

Sr. no. Author Year Pre-processing Technique Diseases Image type Dataset Results Evaluation metrics
1 Korez et al. [33] 2016 3D mesh shape model VB Convolutional neural network 3D spatial VB probability maps Vertebrae shape MRI 23 subjects DC 93.4 ± 1.7% Dice similarity coefficient
2 Arif et al. [34] 2018 Zero padding image dimension 100 × 100 FCN deep probabilistic spatial regression shape-aware deep segmentation Cervical vertebrae X-ray 296 images DC 0.84 Dice similarity coefficient
3 Shi et al. [35] 2018 Coronal spinal centerline intensity curve 3D U-Net Spine CT 61 images DC 0.80 Average dice coefficient
4 Jen-Tang Lu et al. [36] 2018 U-Net architecture spine-curve fitting Multi-class convolutional neural network Lumbar intervertebral discs MRI 4075 patients 0.93 Mean DSC
5 Davison et al. [37] 2018 CNN (CLM and RFRV) weighted heatmap loss Pelvic X-rays 1696 images age 2–11 years 6.92% and 5.85% CLM method, having median error of 100–1000 training set, respectively
6 Kim et al. [38] 2018 Correlation map for ROI Hough transform and canny edge filtering Line-based and graph cut method Lumbar spine MRI 19 images 90% Dice similarity coefficient
7 Rehman et al. [39] 2019 FU-Net framework region-based deep U-Net Osteoporotic CT CSI 2014 CSI 2016 20 images 25 images 92.8 ± 1.9% 95.4 ± 2.1% Dice score of CSI2014 with fractured cases CSI 2016
8 Chuang et al. [40] 2019 3D U-Net and DeconvNet Vertebral lumbar CT xVertSeg.v1 25 Images 88.4% Dice coefficient
9 Lessmann et al. [41] 2019 Random elastic deformations, Gaussian smoothing Fully convolutional neural network Lumbar spine CT CSI14 xVertSeg.v1 15 images 15 images 94.9 ± 2.1% Average dice score
10 Aubert et al. [42] 2019 Statistical shape modeling PCA Simplified parametric model (CNN) Scoliosis X-rays 400 images 1.6 (1.3) mm, 1.8 (1.3) mm, and 2.3 (1.4) mm Location error from 3D Euclidean distances vertebral center endplate centers pedicle centers
11 Pasha et al. [43] 2019 3D coordinate of superior and inferior endplates centroids 3D reconstruction of spine curve agglomerative hierarchical clustering Adolescent idiopathic scoliosis (AIS) X-rays 103 AIS images 20 normal 44% 56% Maximum dissimilarity hypo-thoracolumbar kyphotic flat sagittal profile
12 Chen et al. [44] 2019 Fully convolutional neural network 3D HMM Vertebrae identification and localization CT scans MICAAI challenge 242+60 images 87.97% 2.56 mm Mean identification rate mean error distance
13 Pastor et al. [45] 2020 Decision forest morphological operational refinement Vertebrae identification and localization CT scans 232 images 79.6% Identification rate
14 Vergari et al. [46] 2020 CNN inspired by LeNet-5 Scoliosis Radiographs 1892 images training 204 images validation 96.5% Correctly classified average accuracy
15 Alharbi et al. [47] 2020 CLAHE method ResNet CNN Cobb angle with center points Scoliosis X-rays 243 images 90% Accuracy