Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Figure 1: Elastic matching applied to MR scan of the brain obtained during neurosurgery. (a) Slice from an early stage of the surgery; (b) Slice after craniotomy; (c) Deformed image; (d) Difference image before alignment; (e) Difference image after alignment.
Figure 1: Elastic matching applied to MR scan of the brain obtained during neurosurgery. (a) Slice from an early stage of the surgery; (b) Slice after craniotomy; (c) Deformed image; (d) Difference image before alignment; (e) Difference image after alignment.
Figure 1: Segmentation of WMSA. Proton-density-weighted (A) and T2-weighted (B) images at the level of the lateral ventricles of the brain of a clinically definite MS patient. WMSA are visible in the right centrum semiovale. Corresponding segmented images obtained by three-dimensional EM segmentation (C), PVEC pipeline (D), TDS pipeline (E), and TDSϩ pipeline (F) (tissue labels: gray matter ϭ gray, white matter ϭ white, WMSA ϭ red, CSF ϭ blue). The EM segmentation algorithm generated a preliminary segmentation of gray matter, white matter, WMSA, and CSF. Spurious WMSA were found at the boundaries between CSF and brain parenchyma (type I misclassification), while a large part of the WMSA were classified as gray matter (type II misclassification) (C). PVEC eliminated most type I misclassifications (D), whereas TDS corrected type II misclassifications and part of type I misclassifications (E). While a few spurious WMSA persisted, the TDSϩ pipeline corrected most of the two kinds of error, and generated much better segmentations of WMSA (F).
Figure 1: Segmentation of WMSA. Proton-density-weighted (A) and T2-weighted (B) images at the level of the lateral ventricles of the brain of a clinically definite MS patient. WMSA are visible in the right centrum semiovale. Corresponding segmented images obtained by three-dimensional EM segmentation (C), PVEC pipeline (D), TDS pipeline (E), and TDSϩ pipeline (F) (tissue labels: gray matter ϭ gray, white matter ϭ white, WMSA ϭ red, CSF ϭ blue). The EM segmentation algorithm generated a preliminary segmentation of gray matter, white matter, WMSA, and CSF. Spurious WMSA were found at the boundaries between CSF and brain parenchyma (type I misclassification), while a large part of the WMSA were classified as gray matter (type II misclassification) (C). PVEC eliminated most type I misclassifications (D), whereas TDS corrected type II misclassifications and part of type I misclassifications (E). While a few spurious WMSA persisted, the TDSϩ pipeline corrected most of the two kinds of error, and generated much better segmentations of WMSA (F).
Figure 3: Asymptomatic 60-year-old man with history of transitional cell carcinoma and anterior wall polyp identified on conventional cystoscopy.
Figure 3: Asymptomatic 60-year-old man with history of transitional cell carcinoma and anterior wall polyp identified on conventional cystoscopy.
Figure 5-6: Visualization of surface and volumetric deformation fields.
Figure 5-6: Visualization of surface and volumetric deformation fields.
Figure 2: Map of differences for white matter signal abnormality between mobility groups.
Figure 2: Map of differences for white matter signal abnormality between mobility groups.
Figure 1: When viewed in multiple sections, the appearance of the association fibers on the principal eigenvector maps is consistent with known anatomy.
Figure 1: When viewed in multiple sections, the appearance of the association fibers on the principal eigenvector maps is consistent with known anatomy.
Figure 1: Correlation matrix approximated by first component of singular value decomposition. The values represent the outer product of the transposed matrix, multiplied by the singular value, which is 2.959(d<sub>1</sub>).
Figure 1: Correlation matrix approximated by first component of singular value decomposition. The values represent the outer product of the transposed matrix, multiplied by the singular value, which is 2.959(d1).
Figure 14: 3D surface renderings of deformable surfaces with color-coded intensity of the deformation field. a) brain surface, b)lateral ventricles.
Figure 14: 3D surface renderings of deformable surfaces with color-coded intensity of the deformation field. a) brain surface, b)lateral ventricles.
Figure 2: Three-dimensional mesh representation of finite element model of CG (red) and PZ (dark blue) of the prostate, shown against normal pelvic anatomy (bladder and ureter (blue); vas deferens and seminal vesicles (yellow); and, endorectal coil (black) in rectum (brown)).
Figure 2: Three-dimensional mesh representation of finite element model of CG (red) and PZ (dark blue) of the prostate, shown against normal pelvic anatomy (bladder and ureter (blue); vas deferens and seminal vesicles (yellow); and, endorectal coil (black) in rectum (brown)).
Figure 3: Registration/Segmentation of 3D MR and CT Images: Initial (left), intermediate (middle), and final (right) results. The top row shows the evolving surface. The bottom two rows shows cross-sections of the evolving surface overlaid on an MR, and CT slice, respectively.
Figure 3: Registration/Segmentation of 3D MR and CT Images: Initial (left), intermediate (middle), and final (right) results. The top row shows the evolving surface. The bottom two rows shows cross-sections of the evolving surface overlaid on an MR, and CT slice, respectively.
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