VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images

• URL: http://arxiv.org/abs/2207.05476v1
• Date: Tue, 12 Jul 2022 11:43:33 GMT
• Title: VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images
• Authors: Yao Chen and Yuanhan Mo and Aimee Readie and Gregory Ligozio and Thibaud Coroller and Bartlomiej W. Papiez
• Abstract summary: VertXNet is an ensemble method to automatically segment and label vertebrae in X-ray spinal images. It can be used to circumvent the lack of annotated vertebrae without requiring human expert review.
• Score: 4.310687588548587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challenge by proposing an ensemble method called VertXNet, to automatically segment and label vertebrae in X-ray spinal images. VertXNet combines two state-of-the-art segmentation models, namely U-Net and Mask R-CNN to improve vertebrae segmentation. A main feature of VertXNet is to also infer vertebrae labels thanks to its Mask R-CNN component (trained to detect 'reference' vertebrae) on a given spinal X-ray image. VertXNet was evaluated on an in-house dataset of lateral cervical and lumbar X-ray imaging for ankylosing spondylitis (AS) patients. Our results show that VertXNet can accurately label spinal X-rays (mean Dice of 0.9). It can be used to circumvent the lack of annotated vertebrae without requiring human expert review. This step is crucial to investigate clinical associations by solving the lack of segmentation, a common bottleneck for most computational imaging projects.

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