CPR - curved planar reformation

Kanitsar,; Fleischmann, Dominik; Wegenkittl,; Felkel,; Groller,

doi:10.1109/visual.2002.1183754

Cited by 126 publications

(117 citation statements)

References 11 publications

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“…In a second step, curved planar reconstruction (CPR) [30] is used to create a panoramic view of the nerve along its centreline (see Figure 6 b). A first identification of the nerve's borders is automatically suggested to the user.…”

Section: Anatomical Structures Segmentationmentioning

confidence: 99%

Surgical planning tool for robotically assisted hearing aid implantation

et al. 2013

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Abstract:Purpose: For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT (CBCT)) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed.

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Section: Anatomical Structures Segmentationmentioning

confidence: 99%

Surgical planning tool for robotically assisted hearing aid implantation

et al. 2013

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“…A native plane (a) cannot represent all structures of the placenta at once. Therefore, we use our segmentation method (b), correct the motion in this area using [3], project the placenta mask into the the resulting isotropically resolved volume (c), extract the mean curvature flow skeleton [10] (black lines in (d)), use the resulting points to support a curved surface plane (e) and visualize this plane with curved planar reformation [5] (f). The plane in (f) covers only relevant areas, hence gray value mapping can be adjusted automatically to emphasis placental structures.…”

Section: Resultsmentioning

confidence: 99%

“…It is supported by a mean-curvature flow skeleton [10] generated from the triangulated polygonal mesh of the placenta segmentation and textured similar to curved planar reformation [5], see Figure 3. Although this part is not evaluated thoroughly, clinicians revealed that such a representation is potentially desirable since it compares well to a flattened placenta after birth.…”

Section: One Input Stackmentioning

confidence: 99%

Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

Alansary

Kamnitsas

Davidson

et al. 2016

Lecture Notes in Computer Science

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Abstract. Recently, magnetic resonance imaging has revealed to be important for the evaluation of placenta's health during pregnancy. Quantitative assessment of the placenta requires a segmentation, which proves to be challenging because of the high variability of its position, orientation, shape and appearance. Moreover, image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus, as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field, so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20-38 weeks achieving a Dice score of 71.95 ± 19.79% for healthy fetuses with a fixed scan sequence and 66.89 ± 15.35% for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.

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“…21 The vertebral region is then segmented around the spinal canal using a vertebral template consisting of substructures of vertebral body, transverse processes, and a spinous process. 20 To partition the spine column into vertebrae, curved planar reformations (CPR) 22 in both sagittal and coronal directions are computed along the centerline of the spinal canal. Aggregated intensity profiles (AIP) along the normal direction of the projected centerline on the sagittal and coronal CPR are computed, and dynamic programming is performed to locate the vertebral disks that correspond to valleys in the AIP.…”

Section: Spine Segmentation and Partitioningmentioning

confidence: 99%