Automated quantification of peripheral nerve lesions on MR neurography using deep learning

A deep learning model (nnU-Net) automatically segmented peripheral nerves with Dice 0.97 and lesions with surface Dice 0.64–0.77 on MR neurography; automated total lesion load correlated with nerve conduction velocity (r=-0.62, p<0.001).

• In 178 retrospective MRN exams, the framework generalized across two independent test sets with lesion surface Dice 0.64–0.77.
• Automated lesion load correlated significantly with tibial nerve conduction velocity (r=-0.62, p<0.001) and compound motor action potential (r=-0.568, p=0.002).

Automated summary

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