National Alliance for Medical Image Computing

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in
Collection:
Items displayed:

Gallery of images uploaded to the Publication Database.
449 images found.

PAGE 1 of 45
Figure 1: Visualization of the sub-cortical structure segmentations using the ITK-SNAP segmentation tool. ITK-SNAP was used for the fully manual segmentations of the amygdala, putamen, and globus pallidus in addition to the semi-automatic caudate segmentation. All of the structures studied in this paper are shown in a healthy adult dataset.
Figure 1: Visualization of the sub-cortical structure segmentations using the ITK-SNAP segmentation tool. ITK-SNAP was used for the fully manual segmentations of the amygdala, putamen, and globus pallidus in addition to the semi-automatic caudate segmentation. All of the structures studied in this paper are shown in a healthy adult dataset.
Figure 1: Segmented white matter fiber tracts superimposed on fractional anisotropy images. (A) From left to right: uncinate fasciculus, arcuate fasciculus, cingulum bundle; and (B) from left to right: inferior longitudinal fasciculus, inferior occipitofrontal fasciculus, corticospinal tract; (C) from left to right: corpus callosum (whole structure with fibers color-coded according to similarity of shape and location), genu of corpus callosum (CC1) selected in red, premotor and supplementary motor fibers of corpus callosum (CC2) selected in red; and (D) from left to right: motor fibers of corpus callosum (CC3) selected in red, sensory fibers of corpus callosum (CC4) selected in red, and parietal, temporal and occipital fibers of corpus callosum (CC5) selected in red.
Figure 1: Segmented white matter fiber tracts superimposed on fractional anisotropy images. (A) From left to right: uncinate fasciculus, arcuate fasciculus, cingulum bundle; and (B) from left to right: inferior longitudinal fasciculus, inferior occipitofrontal fasciculus, corticospinal tract; (C) from left to right: corpus callosum (whole structure with fibers color-coded according to similarity of shape and location), genu of corpus callosum (CC1) selected in red, premotor and supplementary motor fibers of corpus callosum (CC2) selected in red; and (D) from left to right: motor fibers of corpus callosum (CC3) selected in red, sensory fibers of corpus callosum (CC4) selected in red, and parietal, temporal and occipital fibers of corpus callosum (CC5) selected in red.
Figure 7:Top row: qualitative comparison of the automatic segmentation results obtained using different atlases. The differences are mostly localized to subcortical GM. Bottom row: GM/WM/CSF segmentation for the R1 region used in the evaluation. Note inconsistencies in CSF segmentation by the raters.
Figure 7:Top row: qualitative comparison of the automatic segmentation results obtained using different atlases. The differences are mostly localized to subcortical GM. Bottom row: GM/WM/CSF segmentation for the R1 region used in the evaluation. Note inconsistencies in CSF segmentation by the raters.
Figure 9: Diagram of striatal afferent projections from cortex. Schematic showing anatomy of the striatum (a) Representative lateral (b) and medial (c) illustrations of cortical areas and their connections to the striatum. The colored segment in the striatum represents the area of the striatum receiving projections from the cortical area of the same color. Abbreviations: AC, anterior cingulate cortex; dlPFC, dorsal lateral prefrontal nial volume as a covariate (accepting that this is a proxy value for cortex; FEF, frontal eye field; IOFC, lateral orbitofrontal cortex; MC, motor cortex; PPC, posterior parietal cortex; SMA, supplementary motor area; SSC, somatosensory cortex. Compiled from Alexander et al. (1986). (Reproduced with permission from Utter and Basso, 2008, Figure 2.)
Figure 9: Diagram of striatal afferent projections from cortex. Schematic showing anatomy of the striatum (a) Representative lateral (b) and medial (c) illustrations of cortical areas and their connections to the striatum. The colored segment in the striatum represents the area of the striatum receiving projections from the cortical area of the same color. Abbreviations: AC, anterior cingulate cortex; dlPFC, dorsal lateral prefrontal nial volume as a covariate (accepting that this is a proxy value for cortex; FEF, frontal eye field; IOFC, lateral orbitofrontal cortex; MC, motor cortex; PPC, posterior parietal cortex; SMA, supplementary motor area; SSC, somatosensory cortex. Compiled from Alexander et al. (1986). (Reproduced with permission from Utter and Basso, 2008, Figure 2.)
Figure 3: This figure illustrates how the National Alliance for Medical Image Computing (NA-MIC) kit, which contains a modular set of interoperable free open source software packages, is managed under a collaborative, high quality software engineering methodology. The kit itself consists of the 3D slicer application software, a number of tools and toolkits such as Visualization Tool kit (VTK) and Insight Segmentation and Registration Tool kit (ITK), and a software engineering methodology that enables multiplatform implementations. DBP, driving biological projects.
Figure 3: This figure illustrates how the National Alliance for Medical Image Computing (NA-MIC) kit, which contains a modular set of interoperable free open source software packages, is managed under a collaborative, high quality software engineering methodology. The kit itself consists of the 3D slicer application software, a number of tools and toolkits such as Visualization Tool kit (VTK) and Insight Segmentation and Registration Tool kit (ITK), and a software engineering methodology that enables multiplatform implementations. DBP, driving biological projects.
Figure 2: Three-dimensional models of TBI anatomy for Patient 1 (acute baseline time point), as generated in 3D Slicer. Each row displays one of six canonical views of the brain, whereas each column corresponds to a structure type (ventricles, non-hemorrhagic lesions, hemorrhagic lesions, and full model). In (A), ventricles are shown as opaque, whereas extracerebral injuries, gray matter (GM) and white matter (WM) volumes are shown as transparent to facilitate visualization of each structure in relationship to cortical landmarks. In (B–D), ventricles are also shown as transparent for similar reasons. Arrows indicate TBI-related pathology of interest (see text for elaboration).
Figure 2: Three-dimensional models of TBI anatomy for Patient 1 (acute baseline time point), as generated in 3D Slicer. Each row displays one of six canonical views of the brain, whereas each column corresponds to a structure type (ventricles, non-hemorrhagic lesions, hemorrhagic lesions, and full model). In (A), ventricles are shown as opaque, whereas extracerebral injuries, gray matter (GM) and white matter (WM) volumes are shown as transparent to facilitate visualization of each structure in relationship to cortical landmarks. In (B–D), ventricles are also shown as transparent for similar reasons. Arrows indicate TBI-related pathology of interest (see text for elaboration).
Figure 3: Example segmentation results for one image slice: (a) groupwise and (b) pair-wise local label fusion, (c) groupwise and (d) pairwise majority voting, and (e) para- metric EM-segmentation.
Figure 3: Example segmentation results for one image slice: (a) groupwise and (b) pair-wise local label fusion, (c) groupwise and (d) pairwise majority voting, and (e) para- metric EM-segmentation.
Figure 4: Visualizing mean differences between normal and ischemic groups (blue denotes expansion and yellow denotes contraction) using [1] (top row) and the proposed method (bottom row)
Figure 4: Visualizing mean differences between normal and ischemic groups (blue denotes expansion and yellow denotes contraction) using [1] (top row) and the proposed method (bottom row)
Figure 5: Top: Original Mesh of a pelvis instance, Bottom: Zoomed view of mesh in two regions. The left most views show the region on the original pelvis, the middle view shows the region with updates using the closest points in iteration 1 (Black dashed curve in Figure 3), the right most views show the region after iteration 3 with adaptive fractional updates (Pink curve in Figure 4). Note that although the metrics are similar, there is a lot more shape distortion in the closest point reconstruction of iteration 1. This shows that the fractional update method is also limiting the shape distortions.
Figure 5: Top: Original Mesh of a pelvis instance, Bottom: Zoomed view of mesh in two regions. The left most views show the region on the original pelvis, the middle view shows the region with updates using the closest points in iteration 1 (Black dashed curve in Figure 3), the right most views show the region after iteration 3 with adaptive fractional updates (Pink curve in Figure 4). Note that although the metrics are similar, there is a lot more shape distortion in the closest point reconstruction of iteration 1. This shows that the fractional update method is also limiting the shape distortions.
Figure 1: (a) Axial slices from 2 examples of 3D T1w brain MR images in the 259-brain database (see text). (b) Coded edge maps associated with (a). (c)–(e) 3 nearest neighbors (NNs), for the images in (a), found by the proposed method. The NNs clearly reflect the overall shape of the skull, ventricles, etc. SPM similarities between the top and bottom row images are very small, reflecting the large differences in skull shape.
Figure 1: (a) Axial slices from 2 examples of 3D T1w brain MR images in the 259-brain database (see text). (b) Coded edge maps associated with (a). (c)–(e) 3 nearest neighbors (NNs), for the images in (a), found by the proposed method. The NNs clearly reflect the overall shape of the skull, ventricles, etc. SPM similarities between the top and bottom row images are very small, reflecting the large differences in skull shape.
PAGE 1 of 45 1 2 3 4 ...45