A Medical Software System for Volumetric Analysis of Cerebral Pathologies in Magnetic Resonance Imaging (MRI) Data
ABSTRACT In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.
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ABSTRACT: This study aimed to assess the accuracy of time-of-flight magnetic resonance angiography, computed tomography, and conventional angiography in depicting the actual length of the blood vessels. Three-dimensional time-of-flight magnetic resonance angiography and computed tomography angiography were performed using a flow phantom model that was 2.11 mm in diameter and had a total area of 0.26 cm(2). After this, volume rendering technique and the maximum intensity projection method as well as two-dimensional digital subtraction angiography and three-dimensional rotational angiography based on conventional angiography were conducted. For three-dimensional time-of-flight magnetic resonance angiography, 8 channel sensitivity encoding (SENSE) head coil for the 3.0 Tesla equipment was used. Fluid was added to the normal saline solution at various rates, such as 11.4, 20.0, 31.4, 40.0, 51.5, 60.0, 71.5, 80.1, 91.5, and 100.1 cm/s using an automatic contrast media injector. Each image was thoroughly examined. After reconstructing the image using the maximum intensity projection method, the length of the conduit in the center of the coronal plane was measured 30 times. After performing computed tomography angiography with the 64-channel CT scanner and 16-channel CT scanner, the images were sent to TeraRecon. Then, the length of the conduit in the center of the coronal plane of each image was measured 30 times after reconstructing the images using volume rendering and maximum intensity projection techniques. For conventional angiography, three-dimensional rotational angiography and two-dimensional digital subtraction angiography were used. Images obtained by three-dimensional rotational angiography were reconstructed and enhanced by 33, 50, and 100 % in the 128 Matrix and the 256 Matrix, respectively on the Xtra Vision workstation. The maximum intensity projection was used for the reconstruction, and the length of the conduit was measured 30 times in the center of the coronal plane of each image. Measurements using the two-dimensional digital subtraction angiography were obtained 30 times in the center of the image. As a result, the lumen length measured by three-dimensional enhanced flow MR angiography images was a minimum of 2.51 ± 0.12 mm when the fluid velocity was 40 cm/s. The images obtained by computed tomography angiography were larger than the actual images obtained by using the test equipment and the reconstruction method. Among the reconstruction methods of three-dimensional rotational angiography, the lumen length in the image reconstructed by 100 % in the 256 matrix was the smallest; 2.76 ± 0.009 mm. In the 128 matrix, as the scope of reconstruction was widened, the length of the vessel was increased by 0.710 units. In the 256 matrix, as the scope of reconstruction was widened, the length of the vessel was decreased by 0.972 units. In two-dimensional digital subtraction angiography, the lumen length in the image was 2.22 ± 0.095 mm. Although this image was magnified similar to the image reconstructed by 100 % in the 256 matrix of three-dimensional rotational angiography (P < 0.05), it was closest to the actual image among the images compared in this study. In conclusion, images obtained by two-dimensional digital subtraction angiography were closer to the actual images compared to the images obtained by other procedures. It can be concluded that vascular images obtained by magnetic resonance angiography, CT angiography, and conventional angiography were larger than the actual images.Journal of Medical Systems 12/2014; 38(12):146. DOI:10.1007/s10916-014-0146-6 · 1.37 Impact Factor
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ABSTRACT: In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Δ. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.PLoS ONE 04/2014; 9(4):e93389. DOI:10.1371/journal.pone.0093389 · 3.53 Impact Factor
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ABSTRACT: Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JMI) criterion was used to extract the optimal features of hemodynamic responses. And extreme learning machine (ELM) was employed to be the classifier. ELM solved the major bottleneck of feedforward neural networks in learning speed, this classifier was easily implemented and less sensitive to specified parameters. The 2-class fNIRS-BCI system was firstly built with an average accuracy of 76.7 %, when all force and speed tasks were categorized as one class, respectively. The multi-class systems based on different levels of force and speed attempted to be investigated, the accuracies were moderate. This study provided a novel paradigm for establishing fNIRS-BCI system, and provided a possibility to produce more degrees of freedom in BCI system.Journal of Medical Systems 05/2015; 39(5):236. DOI:10.1007/s10916-015-0236-0 · 1.37 Impact Factor