Vertebrae localization and spine segmentation on radiographic images for feature‐based curvature classification for scoliosis

Fatima, Joddat; Mohsan, Mashood; Jameel, Amina; Akram, M. Usman; Syed, Adeel M.

doi:10.1002/cpe.7300

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…leveraged YOLOv5 to detect mammofacial fractures in a substantial dataset, classifying fracture conditions into frontal, mid-facial, jaw fractures, and no fractures. Mushtaq et al [34] . demonstrated the YOLOv5 model's proficiency in lumbar vertebrae localization, achieving an impressive average accuracy of 0.975.…”

Section: Related Workmentioning

confidence: 99%

WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

Chen,

Liu,

et al. 2024

Preprint

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Presented herein is the WCAY (Weighted Channel attention YOLO) model, meticulously crafted to identify fracture features across diverse X-ray image sites. This model integrates novel core operators and an innovative attention mechanism to enhance its efficacy. Initially, leveraging the benefits of DSConv (Dynamic Snake Convolution), adept at capturing elongated tubular structural features, we introduce the DSC-C2f module to augment the model's fracture detection performance by replacing a portion of C2f. Subsequently, we integrate the newly proposed Weighted Channel attention (WCA) mechanism into the architecture to bolster feature fusion and improve fracture detection across various sites. Comparative experiments were conducted, evaluating the performance of several attention mechanisms. These enhancement strategies were validated through experimentation on public X-ray image datasets (FracAtlas and GRAZPEDWRI-DX). Multiple experimental comparisons substantiate the model's efficacy, demonstrating its superior accuracy and real-time detection capabilities. According to the experimental findings, on the FracAtlas dataset, our WCAY model exhibits a notable 8.8% improvement in mean Average Precision (mAP) over the original model. On the GRAZPEDWRI-DX dataset, the mAP reaches 64.4%, with a detection accuracy of 93.9% for the "fracture" category alone. The proposed model represents a substantial advancement over the original algorithm when compared to other state-of-the-art object detection models. The code is publicly available at https://github.com/cccp421/Fracture-Detection-WCAY .

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Section: Related Workmentioning

confidence: 99%

WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

Chen,

Liu,

et al. 2024

Preprint

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“…Moreover, Joddat Fatima et al segmented the spinal column using Mask RCNN in conjunction with the YOLOv5 method for vertebral localization. The suggested method achieves 94.69% final average classification accuracy [11]. www.ijacsa.thesai.org Machine learning plays a central role in classifying X-ray images for medical diagnosis.…”

Section: Related Workmentioning

confidence: 99%

“…Precision in Formula (11) talks about the accuracy of positive predictions made by the model, emphasizing minimizing false positives. The precision formula is given by: (11) Recall in equation ( 12), a metric crucial in scenarios where identifying true positives is paramount, is defined as: (12)…”

Section: A Dataset and Peformance Metricsmentioning

confidence: 99%

An Approach to Classifying X-Ray Images of Scoliosis and Spondylolisthesis Based on Fine-Tuned Xception Model

Lu,

Nguyen

2024

IJACSA

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The vertebral column is a marvel of biological engineering and it considers a main part of the skeleton in vertebrate animals. In addition, it serves as the central axis of the human body comprising a series of interlocking vertebrae that provide structural support and flexibility. From basic works like bending and twisting to more complex actions such as walking and running, the spine's impact on human life is profound, underscoring its indispensable role in maintaining physical wellbeing and overall functionality. Moreover, in the hard-working schedule of people in modern life, a bunch of diseases impact on vertebral column such as spondylolisthesis and scoliosis. As a result, numerous researches were provided to take a hand in solving or avoiding these illnesses including machine learning. In this study, transfer learning and fine tuning were used for the classification of X-ray images on vertebrae sickness to avoid complex and wasted time in a medical examination process. The dataset for vertebrae illnesses X-ray images was collected at King

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“…It can be seen that the edge-based model has limited precision, and must be physically tuned for each dataset, while the CNN model is autotuned, and has high division effectiveness, and in this manner can be applied to a wide assortment of datasets. An examination of such models was discussed in [ 6 ], from where it can be seen that AI techniques beat direct division models, and consequently are profoundly liked for clinical applications. This model was additionally examined in [ 7 ], wherein division repeatability of thoracic spinal muscle morphology was performed by means of deep learning-based characterization strategies.…”

Section: Related Workmentioning

confidence: 99%

NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets

Mohanty¹,

Allabun

Solanki

et al. 2023

Diagnostics

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Spinal cord segmentation is the process of identifying and delineating the boundaries of the spinal cord in medical images such as magnetic resonance imaging (MRI) or computed tomography (CT) scans. This process is important for many medical applications, including the diagnosis, treatment planning, and monitoring of spinal cord injuries and diseases. The segmentation process involves using image processing techniques to identify the spinal cord in the medical image and differentiate it from other structures, such as the vertebrae, cerebrospinal fluid, and tumors. There are several approaches to spinal cord segmentation, including manual segmentation by a trained expert, semi-automated segmentation using software tools that require some user input, and fully automated segmentation using deep learning algorithms. Researchers have proposed a wide range of system models for segmentation and tumor classification in spinal cord scans, but the majority of these models are designed for a specific segment of the spine. As a result, their performance is limited when applied to the entire lead, limiting their deployment scalability. This paper proposes a novel augmented model for spinal cord segmentation and tumor classification using deep nets to overcome this limitation. The model initially segments all five spinal cord regions and stores them as separate datasets. These datasets are manually tagged with cancer status and stage based on observations from multiple radiologist experts. Multiple Mask Regional Convolutional Neural Networks (MRCNNs) were trained on various datasets for region segmentation. The results of these segmentations were combined using a combination of VGGNet 19, YoLo V2, ResNet 101, and GoogLeNet models. These models were selected via performance validation on each segment. It was observed that VGGNet-19 was capable of classifying the thoracic and cervical regions, while YoLo V2 was able to efficiently classify the lumbar region, ResNet 101 exhibited better accuracy for sacral-region classification, and GoogLeNet was able to classify the coccygeal region with high performance accuracy. Due to use of specialized CNN models for different spinal cord segments, the proposed model was able to achieve a 14.5% better segmentation efficiency, 98.9% tumor classification accuracy, and a 15.6% higher speed performance when averaged over the entire dataset and compared with various state-of-the art models. This performance was observed to be better, due to which it can be used for various clinical deployments. Moreover, this performance was observed to be consistent across multiple tumor types and spinal cord regions, which makes the model highly scalable for a wide variety of spinal cord tumor classification scenarios.

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Vertebrae localization and spine segmentation on radiographic images for feature‐based curvature classification for scoliosis

Cited by 7 publications

References 31 publications

WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

An Approach to Classifying X-Ray Images of Scoliosis and Spondylolisthesis Based on Fine-Tuned Xception Model

NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets

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