Bone metastases are a frequent occurrence with cancer, and early detection can guide the patient’s treatment regimen. Metastatic bone disease can present in density extremes as sclerotic (high density) and lytic (low density) or in a continuum with an admixture of both sclerotic and lytic components. We design a framework to detect and characterize the varying spectrum of presentation of spine metastasis on positron emission tomography/computed tomography (PET/CT) data. A technique is proposed to synthesize CT and PET images to enhance the lesion appearance for computer detection. A combination of watershed, graph cut, and level set algorithms is first run to obtain the initial detections. Detections are then sent to multiple classifiers for sclerotic, lytic, and mixed lesions. The system was tested on 44 cases with 225 sclerotic, 139 lytic, and 92 mixed lesions. The results showed that sensitivity (false positive per patient) was 0.81 (2.1), 0.81 (1.3), and 0.76 (2.1) for sclerotic, lytic, and mixed lesions, respectively. It also demonstrates that using PET/CT data significantly improves the computer aided detection performance over using CT alone.
Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.
Accurate spine segmentation allows for improved identification and quantitative characterization of abnormalities of the vertebra, such as vertebral fractures. However, in existing automated vertebra segmentation methods on computed tomography (CT) images, leakage into nearby bones such as ribs occurs due to the close proximity of these visibly intense structures in a 3D CT volume. To reduce this error, we propose the use of joint vertebra-rib atlases to improve the segmentation of vertebrae via multi-atlas joint label fusion. Segmentation was performed and evaluated on CTs containing 106 thoracic and lumbar vertebrae from 10 pathological and traumatic spine patients on an individual vertebra level basis. Vertebra atlases produced errors where the segmentation leaked into the ribs. The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92.5 ± 3.1% to 93.8 ± 2.1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0.75 ± 0.60mm and 8.63 ± 4.44mm to 0.30 ± 0.27mm and 3.65 ± 2.87mm, respectively.
Vertebral cortex removal through cancellous bone reconstruction (CBR) algorithms on CT has been shown to enhance the detection rate of bone metastases by radiologists and reduce average reading time per case. Removal of the cortical bone provides an unobstructed view of the inside of vertebrae without any anomalous distractions. However, these algorithms rely on the assumption that the cortical bone of vertebrae can be removed without the identification of the endosteal cortical margin. We present a method for the identification of the endosteal cortical margin based on vertebral models and CT intensity information. First, triangular mesh models are created using the marching cubes algorithm. A search region is established along the normal of the surface and the image gradient is calculated at every point along the search region. The location with the greatest image gradient is selected as the corresponding point on the endosteal cortical margin. In order to analyze the strength of this method, ground truth and control models were also created. Our method was shown to have a significantly reduce the average error from 0.80 mm +/- 0.14 mm to 0.65 mm +/- 0.17 mm (p <0.0001) when compared to erosion. This method can potentially improve CBR algorithms, which improve visualization of cancellous bone lesions such as metastases, by more accurately identifying the inner wall of the vertebral cortex.
Degenerative disc disease (DDD) develops in the spine as vertebral discs degenerate and osseous excrescences or outgrowths naturally form to restabilize unstable segments of the spine. These osseous excrescences, or osteophytes, may progress or stabilize in size as the spine reaches a new equilibrium point. We have previously created a CAD system that detects DDD. This paper presents a new system to determine the severity of DDD of individual vertebral levels. This will be useful to monitor the progress of developing DDD, as rapid growth may indicate that there is a greater stabilization problem that should be addressed. The existing DDD CAD system extracts the spine from CT images and segments the cortical shell of individual levels with a dual-surface model. The cortical shell is unwrapped, and is analyzed to detect the hyperdense regions of DDD. Three radiologists scored the severity of DDD of each disc space of 46 CT scans. Radiologists’ scores and features generated from CAD detections were used to train a random forest classifier. The classifier then assessed the severity of DDD at each vertebral disc level. The agreement between the computer severity score and the average radiologist’s score had a quadratic weighted Cohen’s kappa of 0.64.
Degenerative disc disease (DDD) can be identified as hyperdense regions of bone and osseous spur formation in the spine that become more prevalent with age. These regions can act as confounding factors in the search for alternative hyperdense foci such as neoplastic processes. We created a preliminary CAD system that detects DDD in the spine on CT images. After the spine is segmented, the cortical shell of each vertebral body is unwrapped onto a 2D map. Candidates are detected from the 2D map based on their intensity and gradient. The 2D detections are remapped into 3D space and a level set algorithm is applied to more fully segment the 3D lesions. Features generated from the unwrapped 2D map and 3D segmentation are combined to train a support vector machine (SVM) classifier. The classifier was trained on 20 cases with DDD, which were marked by a radiologist. The pre-SVM program detected 164/193 ground truth lesions. Preliminary results showed 69.65% sensitivity with a 95% confidence interval of (64.47%, 73.92%), at an average of 9.8 false positives per patient.
In this paper, we present evaluation results for a novel colonic polyp classification method for use as part of a computed
tomographic colonography (CTC) computer-aided detection (CAD) algorithm. Inspired by the interpretative
methodology of radiologists using 3D fly-through mode in CTC reading, we have developed an algorithm which utilizes
sequences of images (referred to here as videos) for classification of CAD marks. First, we generated an initial list of
polyp candidates using an existing CAD system. For each of these candidates, we created a video composed of a series
of intraluminal, volume-rendered images focusing on the candidate from multiple viewpoints. These videos illustrated
the shape of the polyp candidate and gathered contextual information of diagnostic importance. We calculated the
histogram of oriented gradients (HOG) feature on each frame of the video and utilized a support vector machine for
classification. We tested our method by analyzing a CTC data set of 50 patients from three medical centers. Our
proposed video analysis method for polyp classification showed significantly better performance than an approach using
only the 2D CT slice data. The areas under the ROC curve for these methods were 0.88 (95% CI: [0.84, 0.91]) and 0.80
(95% CI: [0.75, 0.84]) respectively (p=0.0005).
The early detection of bone metastases is important for determining the prognosis and treatment
of a patient. We developed a CAD system which detects sclerotic bone metastases in the spine on
CT images. After the spine is segmented from the image, a watershed algorithm detects lesion
candidates. The over-segmentation problem of the watershed algorithm is addressed by the novel
incorporation of a graph-cuts driven merger. 30 quantitative features for each detection are
computed to train a support vector machine (SVM) classifier. The classifier was trained on 12
clinical cases and tested on 10 independent clinical cases. Ground truth lesions were manually
segmented by an expert. The system prior to classification detected 87% (72/83) of the manually
segmented lesions with volume greater than 300 mm3. On the independent test set, the sensitivity
was 71.2% (95% confidence interval (63.1%, 77.3%)) with 8.8 false positives per case.
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.