Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models

Abstract

The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29 and 0.38, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers.

Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images

“…Studies that used YOLOv5 models have been reported in various medical fields 36 . One study used YOLOv5 models to detect brain abnormalities, and another study sought to detect lumbar spine deformities using YOLOv5 models 37 , 38 . As a confirmatory diagnostic tool, hip ultrasonography is probably the best modality in the hands of a well-trained operator.…”

Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images

“…23 One study used YOLOv5 models to detect brain abnormalities, and another study sought to detect lumbar spine deformities using YOLOv5 models. 24,25 As a con rmed diagnostic tool, hip ultrasonography is probably the best modality in the hands of a welltrained operator. A study reported that the sensitivity and speci city of hip ultrasonography for detecting DDH were 93% and 97%, respectively.…”

X23D—Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data

“…Similar to X-ray calibration, X-ray localization was not covered in this study, given the existence of such methods in the prior art. A similar line of methodologies exists that can segment the projection of vertebrae on input X-rays (e.g., [ 56 , 57 ]). This can be fed into the X23D algorithm as an auxiliary input to assist the reconstruction task (depicted by dashed lines shown in Figure 1 ).…”