Combining PropSeg and a convolutional neural network for automatic spinal cord segmentation in pediatric populations and patients with spinal cord injury

• Published in: International journal of imaging systems and technology Vol. 33; no. 4; pp. 1396 - 1405
• Main Authors: Blanc, Colline, Shahrampour, Shiva, Mohamed, Feroze B., Leener, Benjamin
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2023; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial neural networks; automatic segmentation; Automation; convolutional neural network; Image segmentation; Magnetic resonance imaging; Neural networks; pediatric; Pediatrics; PropSeg; spinal cord; Spinal cord injuries; spinal cord injury
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22859

Segmentation of the spinal cord is an essential process for the accurate delineation of spinal cord structures but can be a tedious task for experts when using manual or semi‐automated tools. On the other hand, existing automatic segmentation algorithms have not been developed with the pediatric or injured spinal cord in mind. This study presents a novel automated segmentation method that combines the flexibility of deterministic approaches and the powerfulness of neural networks, applied to pediatric and injured spinal cord magnetic resonance imaging (MRI) data. The method first applies the PropSeg algorithm several times on small patches of the spinal cord MRI with various initialization parameters. Then, a convolutional neural network concatenates all these small segmentations with the original MR images to compute a final segmentation. Our results demonstrate good performances on the whole spinal cord (Dice score = 0.88 vs. 0.9) while outperforming existing methods on spinal cord injury regions (0.8 vs. 0.63).