Automated and accurate segmentation of the aorta in 3D+time MR image data is important for early detection of connective tissue disorders leading to aortic aneurysms and dissections. A computer-aided diagnosis method is reported that allows the objective identification of subjects with connective tissue disorders from two-phase 3D+time aortic MR images. Our automated segmentation method combines level-set and optimal border detection. The resulting aortic lumen surface was registered with an aortic model followed by calculation of modal indices of aortic shape and motion. The modal indices reflect the differences of any individual aortic shape and motion from an average aortic behavior. The indices were input to a Support Vector Machine (SVM) classifier and a discrimination model was constructed. 3D+time MR image data sets acquired from 22 normal and connective tissue disorder subjects at end-diastole (R-wave peak) and at 45% of the R-R interval were used to evaluate the performance of our method. The automated 3D segmentation result produced accurate aortic surfaces covering the aorta from the left-ventricular outflow tract to the diaphragm and yielded subvoxel accuracy with signed surface positioning errors of -0.09±1.21 voxel (-0.15±2.11 mm). The computer aided diagnosis method distinguished between normal and connective tissue disorder subjects with a classification correctness of 90.1 %.
The specific goal of our research is to develop automated methods for quantitative analysis of tumor cells from microscopic images. By segmenting living tumor cells, cell behavior under stress can be studied. Therefore, accurate acquisition and analysis of microscope images from living cell cultures are of utmost importance. If cell behavior can be studied through their life span, cell motility and shape changes can be quantified and analyzed in relation with the severity of induced stress. Consequently, cell responses to the environment can be quantitatively analyzed. The Large Scale Digital Cell Analysis System developed at the University of Iowa provides a capability for real-time cell image acquisition. In the work presented here, feasibility of fully automated living tumor cell segmentation is demonstrated allowing future quantitative cell studies. An automated method for identification of the cell boundaries in microscopy images is presented.
This work describes a method for detecting mitotic cells in time-lapse microscopy images of live cells. The image sequences are from the Large Scale Digital Cell Analysis System (LSDCAS) at the University of Iowa. LSDCAS is an automated microscope system capable of monitoring 1000 microscope fields over time intervals of up to one month. Manual analysis of the image sequences can be extremely time consuming. This work is part of a larger project to automate the image sequence analysis. A three-step approach is used. In the first step, potential mitotic cells are located in the image sequences. In the second step, object border segmentation is performed with the watershed algorithm. Objects in adjacent frames are grouped into object sequences for classification. In the third step, the image sequences are converted to feature vector sequences. The feature vectors contain spatial and temporal information. Hidden Markov Models (HMMs) are used to classify the feature vector sequences into dead cells, cell edges, and dividing cells. Discrete and continuous HMMs were trained on 500 sequences. The discrete HMM recognition rates were 62% for dead cells, 77% for cell edges, and 75% for dividing cells. The continuous HMM results were 68%, 88% and 77%.
Cardiovascular events frequently result from local rupture of vulnerable atherosclerotic plaque. Non-invasive assessment of plaque vulnerability is needed to allow institution of preventive measures before heart attack or stroke occur. A computerized method for segmentation of arterial wall layers and plaque from high-resolution volumetric MR images is reported. The method uses dynamic programming to detect optimal borders in each MRI frame. The accuracy of the results was tested in 62 T1-weighted MR images from 6 vessel specimens in comparison to borders manually determined by an expert observer. The mean signed border positioning errors for the lumen, internal elastic lamina, and external elastic lamina borders were -0.12±0.14 mm, 0.04±0.12mm, and -0.15±0.13 mm, respectively. The presented wall layer segmentation approach is one of the first steps towards non-invasive assessment of plaque vulnerability in atherosclerotic subjects.