Osteoporosis is a systemic disorder that reduces the bone mineral density, increasing the vertebrae's fragility and proneness to fracture. Although the bone densitometry index t-Score is a solid marker for the osteoporosis diagnosis, its measure alone is insufficient to predict the future development of fragility fractures. A complementary approach to address vertebral bone characterization is the analysis of magnetic resonance imaging (MRI) by radiomic features, which model vertebral bodies' morphological properties after color and texture. Radiomic features have been employed for detecting fragility fractures in related work, but, to the best of our knowledge, no study has been conducted on their suitability to recover similar, diagnosed cases that could hint at future fractures. We fulfill this gap by designing a Content-based Image Retrieval (CBIR) tool with embedded radiomic features, which uses past cases recovered from an annotated database to (i) identify an existing fragility fracture in a query vertebra and (ii) predict a fracture to a query vertebra from an aging patient. The proposed CBIR was evaluated on a reference database of 273 vertebral bodies from sagittal T2-weighted MRIs. The results indicate our fine-tuned approach spotted fragility fractures accurately$(\mathrm{F}1-\text{Score} =0.83,\ \text{Precision} =0.83,\ \text{AUC} =0.81,\ \text{CI} =95\%)$. We also investigated the CBIR potential to predict fractures in a case study regarding three patients from the reference database (confirmed osteoporosis, MRI in [2012–2017]). The system correctly inferred the prediction of future fractures for query vertebrae, which were confirmed a few years later (MRI in [2018–2021]). Such empirical findings suggest CBIR can support a differential diagnosis in the assessment of local fragility fractures.

Bone mineral density (BMD) is the international standard for evaluating osteoporosis/osteopenia. The success rate of BMD alone in estimating the risk of vertebral fragility fracture (VFF) is approximately 50%, making BMD far from ideal in predicting VFF. In addition, whether or not a patient has been diagnosed with osteoporosis or osteopenia, he or she may suffer a VFF. For this reason, we conducted an extensive empirical study to assess VFFs in postmenopausal women. We considered a representative dataset of 94 T1- and T2-weighted routine spine MRI (with osteopenia or osteoporosis), split into 2,400 samples (slices). Comparing the classification results of machine learning and deep learning (DL) techniques showed that DL generally achieved better results at the cost of higher computational power and hard explainability. ResNet achieved the best results in discriminating patients from groups with and without VFFs with 83% accuracy and 90% AUC (with a confidence interval of 99%). Our results represent a significant step toward prospective and longitudinal studies investigating methods to achieve higher accuracy in predicting VFFs based on spine MRI features of vertebrae without fracture.

Bone densitometry (DEXA) is the international reference standard to evaluate Bone Mineral Density (BMD) and diagnose osteoporosis. However, DEXA is far from ideal when used to predict fragility fractures, which are strongly related to morbidity and mortality. According to the literature, spine MRI texture features correlate well with DEXA measurements. For this reason, we conducted an extensive empirical study aimed at assessing fragility fractures secondary to osteoporosis. To perform the evaluations, we developed a radiomic-based approach called BEAUT (BonE Analysis Using Texture). We performed experiments on a meaningful database composed of 47 T2-weighted sagittal sequences from lumbar spine MRI. The patients were diagnosed with osteopenia or osteoporosis according to DEXA (patients with low bone mass). BEAUT achieved an accuracy of 92% and 97% AUC with feature selection to discriminate between patients from groups `Fractures' and `No Fractures'. The results support claiming that texture features potentially discriminate subjects with bone mass loss, spotting those at risk of fragility fractures.

Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images. We also take advantage of the slope coefficient from the annotation time to reduce the total number of annotated slices, reducing the time spent on manual annotation. We show experimental results on a representative dataset with 17 MRI exams demonstrating that our approach significantly outperforms the competitors and, on average, only 37% of the total slices with vertebral body content must be annotated without losing performance/accuracy. Compared to the state-of-the-art methods, we have achieved a Dice Score gain of over 5% with comparable processing time. Moreover, 3DBGrowth works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist.

This paper presents a segmentation approach guided by the user for extracting the vertebral bodies of spine from MRI. The proposed approach, called VBSeg, takes advantage of super pixels to reduce the image complexity and then making easy the detection of each vertebral body contour. Super pixels adapt themselves to the image structures, once their formation law follows the homogeneity of the image regions. However, for some diseases or abnormalities, the boundary of each super pixel does not fit well in the vertebra contour. To avoid this drawback, we propose to use the Otsu's method as a possegmentation step to divide the super pixels into smaller ones. The final segmentation is obtained through a region growing approach using points manually selected by the specialist. It can produce masks of the five lumbar vertebrae with an average precision of 80% and recall of 87%, when compared to the manual segmentation of a trained specialist. These values show that the VBSeg is a valuable asset to assist the medical specialist in the task of vertebral bodies' segmentation, with much less effort and time demand.

Our study aimed to develop a system for computer-aided diagnosis of vertebral compression fractures (VCFs) using magnetic resonance imaging (MRI), to help in the differentiation between malignant and benign VCFs. Lumbar spine MRI was used to acquire T1-weighted images in the sagittal plane. Images from 63 consecutive patients (38 women, 25 men, mean age 62.25 ± 14.13 years) with at least one VCF diagnosis were studied. Contrast and texture features were extracted from manually segmented images of 103 vertebral bodies with VCFs. The classification of malignant vs. benign VCFs was performed using the k-nearest neighbor (KNN) classifier with the Euclidean distance. Using a KNN classifier with k=3, feature selection, and 10-fold cross-validation, we obtained a value of the area under the receiver operating characteristic curve of 0.913.