Interactive Segmentation of Compressed Spinal Canal and Cord in Degenerative Cervical Myelopathy

Study Design: Retrospective Diagnostic Study Objective : We aim to develop an interactive segmentation model that can offer accuracy and reliability for the segmentation of the compressed spinal cord in degenerative cervical myelopathy (DCM). Setting:Boramae Medical Center, Korea. Methods : A dataset of 1,444 frames from 294 MRI records of DCM patients was used and we developed two different segmentation models for comparison: autosegmentation and interactive segmentation. The former was based on U-Net and utilized a pretrained ConvNeXT-tiny as its encoder. For the latter, we employed an interactive segmentation model structured by SimpleClick, a large model that utilizes a vision transformer as its backbone, together with simple fine-tuning. The segmentation performances of the two models were compared in terms of their DICE scores. The efficiency of the interactive segmentation model was evaluated by the number of clicks required to achieve a target mean intersection over union (mIoU). Results : The auto and interactive segmentation models with 10 clicks returned a 0.8226 and 0.9537 DICE score for cases involving canal segmentation and a 0.7363 and 0.7767 DICE score for cases involving cord mask segmentation alone, respectively. The required clicks for the interactive segmentation model to achieve a 90% mIoU for spinal canal with cord cases and 80% mIoU for spinal cord cases were 11.71 and 11.99, respectively. Conclusions : We found that the interactive segmentation model outperformed the autosegmentation model. Simple manual inputs can help the model identify a region of interest in the irregular shape of spinal cord. Sponsorship : No sponsorship