Year: 2025
Type: article
Abstract: Human spine is a complex structure that plays a vital role in the movement, protection, and support of the body so it is very important to follow proper spine bio-mechanics to avoid any unwanted effect on body. Spinal diseases can cause compression or pulling the nerve roots, which can lead to radicular symptoms like back pain or leg pain. On the other hand, it may cause deformities which are most common at C4-C7 and L4-S1 level. Localization of vertebra bones that make up the spine is key in spinal disease diagnosis such as calculating cobb angles, shape detection, detecting vertebra fractures and other abnormalities. In this paper, we have covered four modules, first, for the vertebrae localization we used detection transformer to localize 68 corner points, Secondly, we have used a SegFormer to do the segmentation of the spine. Thirdly, center profile of the spine was generated using center point technique for localization and morphological thinning for segmentation. In the final step of shape analysis process, we take the profile of spine to calculates the features and classify the data into normal, Single-bend (C-shaped) and Double bend (S-shaped) spine. DETR gives mAP value of 0.96 at 0.5 IOU threshold and SegFormer achieves a dice score of 0.93 in segmenting spinal images. For the classification of the data, we have used different classifier (SVM, RF, KNN and NB). We have used three features from both Segformer and DETR techniques. Features acquired from localization technique (DETR) and Segmentation (SegFormer) yield better accuracy when using a random forest classifier. Random forest performs best for AASCE MICCAI 2019 dataset with an accuracy of 98.3%. The MAE 2.7 and SMAPE 4.37 of our proposed approach is slightly good than that of other methodologies.
Source: Scientific Reports
Authors: Syeda Humaira Batool, Noshaba Liaquat, Sajid Gul Khawaja, Norah Saleh Alghamd, Muhammad Usman Akram
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G6016183765
OpenAlex ID: W4414595533