The identification of spondylolysis and spondylolisthesis is important in spinal diagnosis, rehabilitation, and
surgery planning. Accurate and automatic detection of spinal portion with spondylolisthesis problem will
significantly reduce the manual work of physician and provide a more robust evaluation for the spine condition.
Most existing automatic identification methods adopted the indirect approach which used vertebrae locations to measure
the spondylolisthesis. However, these methods relied heavily on automatic vertebra detection which often suffered from
the pool spatial accuracy and the lack of validated pathological training samples. In this study, we present a novel
spondylolisthesis detection method which can directly locate the irregular spine portion and output the corresponding
grading. The detection is done by a set of learning-based detectors which are discriminatively trained by synthesized
spondylolisthesis image samples. To provide sufficient pathological training samples, we used a parameterized spine
model to synthesize different types of spondylolysis images from real MR/CT scans. The parameterized model can
automatically locate the vertebrae in spine images and estimate their pose orientations, and can inversely alter the
vertebrae locations and poses by changing the corresponding parameters. Various training samples can then be generated
from only a few spine MR/CT images. The preliminary results suggest great potential for the fast and efficient
spondylolisthesis identification and measurement in both MR and CT spine images.
This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.
Accurate and efficient patient registration is crucial for the success of image-guidance in open spinal surgery. Recently, we have established the feasibility of using intraoperative stereovision (iSV) to perform patient registration with respect to preoperative CT (pCT) in human subjects undergoing spinal surgery. Although a desired accuracy was achieved, the method required manual segmentation and placement of feature points on reconstructed iSV and pCT surfaces. In this study, we present an improved registration pipeline to eliminate these manual operations. Specifically, automatic geometric rectification was performed on spines extracted from pCT and iSV into pose-invariant shapes using a nonlinear principal component analysis (NLPCA). Rectified spines were obtained by projecting the reconstructed 3D surfaces into an anatomically determined orientation. Two-dimensional projection images were then created with image intensity values encoding feature "height" in the dorsal-ventral direction. Registration between the 2D depth maps yielded an initial point-wise correspondence between the 3D surfaces. A refined registration was achieved using an iterative closest point (ICP) algorithm. The technique was successfully applied to two explanted and one live porcine spines. The computational cost of the registration pipeline was less than 1 min, with an average target registration error (TRE) less than 2.2 mm in the laminae area. These results suggest the potential for the pose-invariant, rectification-based registration technique for clinical application in human subjects in the future.
The method of using holography with the recorded beams modulated by the plate motion to measure 3D displacement is proposed. Displacements of the plate are determined by the reference object. Image signal is processed with PDS microdensitometer.
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