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

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Figure: Snapshot of the 2012 SPL Brain Atlas
Figure: Snapshot of the 2012 SPL Brain Atlas
Figure 3: Sample anatomy, ME and GAME parameters (a) A T2W anatomical image is shown with muscle (MU, green), biopsy target (blue), CG (yellow) and PZ (red) contours outlined, along with maps of (b) R2* (c) R2 and (d) σ obtained from ME and GAME fits to multi-echo data.
Figure 3: Sample anatomy, ME and GAME parameters (a) A T2W anatomical image is shown with muscle (MU, green), biopsy target (blue), CG (yellow) and PZ (red) contours outlined, along with maps of (b) R2* (c) R2 and (d) σ obtained from ME and GAME fits to multi-echo data.
Figure 4: Three-dimensional color maps of distance map between the original tumor and ablation zone are rendered on the surface of ablation zone for the cases with local recurrences. The red areas represent insufficient ablation margin. The 3D models of original (upper) and recurrent (lower) tumors are superimposed. The red areas represent ablation margins with a thickness close to zero.
Figure 4: Three-dimensional color maps of distance map between the original tumor and ablation zone are rendered on the surface of ablation zone for the cases with local recurrences. The red areas represent insufficient ablation margin. The 3D models of original (upper) and recurrent (lower) tumors are superimposed. The red areas represent ablation margins with a thickness close to zero.
Figure 2: Cloud Architecure for Running Speculative Execution of NRR
Figure 2: Cloud Architecure for Running Speculative Execution of NRR
Figure: Snapshot of the Ear Atlas
Figure: Snapshot of the Ear Atlas
Figure 1: Examples depicting the complexity of HNC segmentation in FDG PET scans. [(a)–(c)] Volume rendering of PET scans, showing the primary cancer [arrow in (d)–(i)] and lymph nodes with uptake. [(d)–(f)] Rendering of segmentations of individual lesions shown in (a)–(c) combined with a volume rendering of the corresponding PET volume. [(g)–(i)] Examples of PET cross sections with outlined tumor segmentations. (g) Necrotic primary tumor with metabolically active adjacent lymph node. [(h) and (i)] Cases with multiple lesions and varying degrees of tracer uptake.
Figure 1: Examples depicting the complexity of HNC segmentation in FDG PET scans. [(a)–(c)] Volume rendering of PET scans, showing the primary cancer [arrow in (d)–(i)] and lymph nodes with uptake. [(d)–(f)] Rendering of segmentations of individual lesions shown in (a)–(c) combined with a volume rendering of the corresponding PET volume. [(g)–(i)] Examples of PET cross sections with outlined tumor segmentations. (g) Necrotic primary tumor with metabolically active adjacent lymph node. [(h) and (i)] Cases with multiple lesions and varying degrees of tracer uptake.
Figure 1: Outline of the registration process. Initial T2w prostate image is contoured semi­-automatically, and the result is propagated to the subsequent needle confirmation images using the chain of transformations.
Figure 1: Outline of the registration process. Initial T2w prostate image is contoured semi­-automatically, and the result is propagated to the subsequent needle confirmation images using the chain of transformations.
Figure 1: Diagram of the interaction among the various data sources and processing steps that result in the dataset described in this paper. Components of the dataset represented in DICOM are released publicly within The Cancer Imaging Archive (TCIA) QIN-HEADNECK collection. FOSS tools corresponding to the processing steps other than Reference Region (RR) segmentation (processing steps with the dashed outline) are available.
Figure 1: Diagram of the interaction among the various data sources and processing steps that result in the dataset described in this paper. Components of the dataset represented in DICOM are released publicly within The Cancer Imaging Archive (TCIA) QIN-HEADNECK collection. FOSS tools corresponding to the processing steps other than Reference Region (RR) segmentation (processing steps with the dashed outline) are available.
Figure 3: 3D anatomical models of the upper abdomen. The figure displays the liver (dark yellow), stomach (brown), esophagus (light yellow), spleen (light purple), pancreas (dark pink) and air-filled bowel (cyan), along with the aorta (red), inferior vena cava (blue) and portal vein (blue). The opacity of the bowel has been lowered to allow deeper structures to become visible. These models were created by two fourth-year medical students.
Figure 3: 3D anatomical models of the upper abdomen. The figure displays the liver (dark yellow), stomach (brown), esophagus (light yellow), spleen (light purple), pancreas (dark pink) and air-filled bowel (cyan), along with the aorta (red), inferior vena cava (blue) and portal vein (blue). The opacity of the bowel has been lowered to allow deeper structures to become visible. These models were created by two fourth-year medical students.
Figure 2: (A–C) shows the estimated radius of bounded trajectories, the fraction of the bounded compartment in the dMRI signal and the normalized mean-square error between the estimated signal and the measurements, respectively. (D–F) shows the measured (dot points) signal and the estimated signal (solid lines) in three representative voxels from the genu, mid-body and splenium areas, respectively, with the horizontal axis being absolute value of the inner product between the gradient direction and the axon orientation.
Figure 2: (A–C) shows the estimated radius of bounded trajectories, the fraction of the bounded compartment in the dMRI signal and the normalized mean-square error between the estimated signal and the measurements, respectively. (D–F) shows the measured (dot points) signal and the estimated signal (solid lines) in three representative voxels from the genu, mid-body and splenium areas, respectively, with the horizontal axis being absolute value of the inner product between the gradient direction and the axon orientation.
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