Convolutional Neural Network–Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury

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Fig 1.
Whole spinal cord and lesion segmentation performance by epoch for BASICseg algorithms for the whole spinal cord (A–C) and the lesion (D–F).
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Fig 2.
Sample segmentation outputs for the criterion standard manual segmentation and each model for whole spinal cord (columns 2–5) and lesions (columns 6 and 7) in 5 sample patients. The first column contains the sample axial T2WI on which segmentation was performed.

Table 1:

Summary of patient demographics and injury

Patient Demographics and Injury Characteristics (N = 47)
Age (mean) (SD) (yr)	55 (19)
Sex (M/F)	32:15
Time from injury to MRI (mean) (SD) (hr)	5.4 (4.8)
Admission ASIA Grade
A	10
B	3
C	5
D	21
Unknown^{^a}	8
Admission lower extremity motor score (mean) (SD)	25 (23)
Discharge lower extremity motor score (mean) (SD)	29 (21)

Table 2:

Results from linear mixed modeling

Model	Estimate	SE	df	T Value	P Value
Overall spine segmentation
BASICseg-1 vs Propseg	−0.12166	0.01765	484	−6.893	<.001
BASICseg-1 vs Deepseg	−0.03382	0.01765	484	−1.916	.056
Spine segmentation at levels of lesion
BASICseg-1 vs Propseg	−0.22028	0.04410	102.62	−4.995	<.001
BASICseg-1 vs Deepseg	−0.10254	0.04347	102.62	−2.359	.020
Spine segmentation at normal-appearing level
BASICseg-1 vs Propseg	−0.09525	0.01687	367.5	−5.645	<.001
BASICseg-1 vs Deepseg	−0.01338	0.01694	367.5	−0.790	.43
Relative difference in model segmentation between lesion and normal-appearing levels
Lesion category difference in Propseg compared with BASICseg-1	−0.12249	0.03928	484.4	−3.119	<.001
Lesion category difference in Deepseg compared with BASICseg-1	−0.08689	0.03889	484.4	−2.234	.026