Osteoporotic and neoplastic compression fracture classification on longitudinal CT

Abstract

Classification of vertebral compression fractures (VCF)having osteoporotic or neoplastic origin is fundamental to the planning of treatment. We developed a fracture classification system by acquiring quantitative morphologic and bone density determinants of fracture progression through the use of automated measurements from longitudinal studies. A total of 250 CT studies were acquired for the task, each having previously identified VCFs with osteoporosis or neoplasm. Thirty-six features for each identified VCF were computed and classified using a committee of support vector machines. Ten-fold cross validation on 695 identified fractured vertebrae showed classification accuracies of 0.812, 0.665, and 0.820 for the measured, longitudinal, and combined feature sets respectively.

Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

“…Wang et al suggested a method for acquiring features such as volumetric parameter (morphological) and bone density determinants to classify vertebral compression fractures (VCF) of Osteoporotic origin. 29 Although it is observed from the data that misclassifications produced by longitudinal feature set is more in comparison with other features (Demographic and measured) yet on inclusion of longitudinal feature in committee of SVM, provides ease to accurate classification but advancement is not significant by data.…”

Osteoporosis Detection in Lumbar Spine L1-L4 based on Trabecular Bone Texture Features

“…The kernel is defined in Eq. (12). The following steps are carried out to bring the fine texture information in the image.…”

“…The system making more complex for diagnosing. The features such as volumetric parameter (morphological) and bone density determinants [12] are used to classify Vertebral Compression Fractures (VCF) of Osteoporotic origin of CT images, which provides better classification accuracy. The model gives better shape analysis to discriminate between fractured and normal vertebral [13] using MRI.…”

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

“…(15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection. (15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection.…”

“…Examples of this work include automated detection of traumatic and compression fractures, degenerative changes, epidural masses, bone metastases, and bone mineral density of the spine on CT utilizing both second-and third-generation techniques. (15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection. (39)(40)(41)(42) Magnetic resonance imaging Because MRI is widely used for assessing spinal degenerative changes in the setting of neck and back pain, significant efforts have been made toward automated delineation of spinal anatomy on MRI images.…”