|
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
|
