Purpose:To design and validate a computer system for automated detection and quantitative characterization of sclerotic metastases of the thoracolumbar spine on computed tomography (CT) images. Materials and Methods:This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. The data set consisted of CT examinations in 49 patients (14 female, 35 male patients; mean age, 57.0 years; range, 12-77 years), demonstrating a total of 532 sclerotic lesions of the spine of greater than 0.3 cm 3 in volume, and in 10 control case patients (four women, six men; mean age, 55.2 years; range, 19-70 years) without spinal lesions. CT examinations were divided into training and test sets, and images were analyzed according to prototypical fully-automated computer-aided detection (CAD) software. Free-response receiver operating characteristic analysis was performed. Results:Lesion detection sensitivity on images in the training set was 90%, relative to reference-standard marked lesions (95% confidence interval [CI]: 83%, 97%), at a false-positive rate (FPR) of 10.8 per patient (95% CI: 6.6, 15.0). For images in the testing set, sensitivity was 79% (95% CI: 74%, 84%), with an FPR of 10.9 per patient (95% CI: 8.5, 13.3). Conclusion:This CAD system successfully identified and segmented sclerotic lesions in the thoracolumbar spine.q RSNA, 2013
Vertebral compression fractures can be caused by even minor trauma in patients with pathological conditions such as osteoporosis, varying greatly in vertebral body location and compression geometry. The location and morphology of the compression injury can guide decision making for treatment modality (vertebroplasty versus surgical fixation), and can be important for pre-surgical planning. We propose a height compass to evaluate the axial plane spatial distribution of compression injury (anterior, posterior, lateral, and central), and distinguish it from physiologic height variations of normal vertebrae. The method includes four steps: spine segmentation and partition, endplate detection, height compass computation and compression fracture evaluation. A height compass is computed for each vertebra, where the vertebral body is partitioned in the axial plane into 17 cells oriented about concentric rings. In the compass structure, a crown-like geometry is produced by three concentric rings which are divided into 8 equal length arcs by rays which are subtended by 8 common central angles. The radius of each ring increases multiplicatively, with resultant structure of a central node and two concentric surrounding bands of cells, each divided into octants. The height value for each octant is calculated and plotted against octants in neighboring vertebrae. The height compass shows intuitive display of the height distribution and can be used to easily identify the fracture regions. Our technique was evaluated on 8 thoraco-abdominal CT scans of patients with reported compression fractures and showed statistically significant differences in height value at the sites of the fractures.
This work presents a computer-aided detection (CAD) system to aid radiologists in finding sclerotic bone metastases in the spine on CT images. The spine is first segmented using thresholding, region growing and a vertebra template. A watershed algorithm and a merging routine segment potential lesion candidates in each twodimensional (2-D) axial CT image. Next, overlapping 2-D detections on sequential CT slices are merged to form 3-D candidate lesions. For each of these, 30 quantitative features based on shape, density, and location are computed. After a feature filter eliminates clearly false candidates, a ground truth on 10 clinical cases segmented manually by an expert, and the features of each CAD candidate are used to train seven support vector machines. The segmentation algorithm detects 164 out of the 212 manually segmented lesions. A ten-fold cross-validation trained on these detections results in 77.4% sensitivity at an average of 9.44 false positives per case.
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