Computational techniques to segment and classify lumbar compression fractures

AbstractVertebral fractures are important indicators of osteoporosis. Fractures with partial collapse of vertebral bodies are referred to as vertebral compression fractures (VCFs) that are usually non-traumatic in nature. Some common causes of VCFs are trauma, bone failure related to osteoporosis (benign) and metastatic cancer (malignant). This paper aims at developing a system for computer-aided diagnosis to help in the detection, labeling and segmentation of lumbar vertebral body (VB) and to further classify each VB into normal, malignant and benign VCFs. After the initial preprocessing, morphological, shape and angular features are used in the detection, labeling and segmentation steps. Various shape and statistical texture features are extracted from the segmented VB and are fed to the classifier for the final decision. The segmentation and classification results obtained were compared with the ground truth manual segmentation of the lumbar VB and the decision labels of the fractures provided by the experts. The dice similarity coefficient (DSC) for segmentation reached up to 94.27%, and the classification results show that shape and texture features together are able to correctly classify with an accuracy rate of 95.34%. The final outcomes are expected to be useful in the analysis of vertebral compression fractures.
Source: La Radiologia Medica - Category: Radiology Source Type: research

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Source: Journal of the National Comprehensive Cancer Network : JNCCN - Category: Cancer & Oncology Tags: J Natl Compr Canc Netw Source Type: research
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Source: Current Osteoporosis Reports - Category: Orthopaedics Source Type: research
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Source: European Journal of Cancer - Category: Cancer & Oncology Authors: Tags: POSTERS A: Lifestyle, Prevention including Secondary Prevention Source Type: research
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Source: European Journal of Cancer - Category: Cancer & Oncology Authors: Tags: POSTERS B: Lifestyle, Prevention including Secondary Prevention Source Type: research
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Source: Molecules - Category: Chemistry Authors: Tags: Article Source Type: research
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Source: Journal of Bone and Mineral Research - Category: Orthopaedics Authors: Tags: Original Article Source Type: research
Selective estrogen receptor modulators (SERMs) are an increasingly important therapeutic modality that are used by clinicians on a daily basis. Unfortunately, clinicians have a limited understanding regarding the underlying mechanism(s) of how SERMs function and their increasingly useful role in the treatment of estrogen-responsive target tissues such as the breast, bone, vagina, uterine endometrium, and brain. This review will provide a basic understanding of our current knowledge of SERM pharmacodynamics and will highlight the clinical applications of Food and Drug Administration–approved SERMs in the treatment of ...
Source: Menopause - Category: OBGYN Tags: Clinical Corner: Invited Review Source Type: research
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Source: Cancer Medicine - Category: Cancer & Oncology Authors: Tags: ORIGINAL RESEARCH Source Type: research
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Source: Frontiers in Endocrinology - Category: Endocrinology Source Type: research
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