Live-Wire Revisited

Salah, Zein; Orman, Jasmina; Bartz, Dirk

doi:10.1007/3-540-26431-0_33

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Methods for extending the live-wire for the application on 3D datasets have been presented in [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

“…This is very useful for moving the segmentation boundary away from risk structures. Moreover, we provide options for interpolating the segmentation between well-segmented (or corrected) key slices, as well as for computing live-wire contours utilizing adaptive propagation, as described in [18], thus avoiding the interactive correction of every slice.…”

Section: Slice-based Refinementmentioning

confidence: 99%

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Preoperative planning of a complete mastoidectomy: semiautomatic segmentation and evaluation

Salah

Kästner²,

Dammann³

et al. 2006

Int J CARS

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Objective Preoperative planning of complete mastoidectomy based on postprocessing, interactive processing, and visualization of CT datasets was implemented and tested. The synthesized images are used to define an access path for the operative treatment of diseases at the lateral skull base. Alternatively, the mastoid may be resected to allocate space for implants such as hearing aids. Methods For the preoperative planning of such surgery, accurate segmentation of the mastoid is required. A semiautomatic pipeline for segmentation of the mastoid was developed. The approach requires minimal user interaction, using a method based on generating a fast 3D-oriented primary segmentation, which (if necessary) can be refined interactively, both in 3D-and slice-based manner. Results To verify the usability of the segmentation and visualization tools for surgery planning, an evaluation study was conducted with datasets of 16 operated patients. For each patient, at least on preoperative CT scan of the skull, with focus on the region of the mastoid to be operated, was available. Qualitative and quantitative evaluation of the images was performed, and the results were tabulated. Conclusion Semiautomated mastoid segmentation provides a feasible means for preoperative planning, based on postprocessing analysis and visualization of CT datasets.

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“…Methods for extending the live-wire for the application on 3D datasets have been presented in [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Section: Slice-based Refinementmentioning

confidence: 99%

Preoperative planning of a complete mastoidectomy: semiautomatic segmentation and evaluation

Salah

Kästner²,

Dammann³

et al. 2006

Int J CARS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Approaches for iterative seed point based live-wire boundary transfer in volumetric data sets have been introduced in Refs. [17,18].…”

Section: Introductionmentioning

confidence: 97%

Live-wire-based segmentation using similarities between corresponding image structures

Färber

Ehrhardt

Handels

2007

Computerized Medical Imaging and Graphics

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“…König et al [32,33] applied Mortensen's 2D live-wire work to a 3D surface, but their method focused on assembling surface parts and performance depended greatly on 3D oversegmentation results. The approach of Salah et al [34] used the 2D live wire to propagate seeds to neighboring sections. But since the approach only searches around a seed's 8-neighbors to locate a point on a new section, it cannot guarantee accuracy if contours change greatly from one section to the next or if the image data is noisy.…”

Section: Introductionmentioning

confidence: 99%

Interactive segmentation based on the live wire for 3D CT chest image analysis

Lü

Higgins

2007

Int J CARS

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Object The definition of regions of interest (ROIs) such as suspect cancer nodules or lymph nodes in 3D MDCT chest images is often difficult because of the complexity of the phenomena that give rise to them. Manual slice tracing has been used commonly for such problems, but it is extremely time consuming, subject to operator biases, and does not enable reproducible results. Proposed automated 3D imagesegmentation methods are generally application dependent, and even the most robust methods have difficulty in defining complex ROIs. Materials and methods The semi-automatic interactive paradigm known as live wire has been proposed by researchers, whereby the human operator interactively defines an ROI's boundary, guided by an active automated method. We propose 2D and 3D live-wire methods that improve upon previously proposed techniques. The 2D method gives improved robustness and incorporates a search region to improve computational efficiency. The 3D method requires the operator to only consider a few 2D slices, with an automated procedure performing the bulk of the analysis. Results For tests run with five human operators on both 2D and 3D ROIs in 3D MDCT chest images, the reproducibility was >97% and the ground-truth correspondence was at least 97%. The 2D live-wire approach was ≥14 times faster than manual slice tracing, while the 3D method was ≥28 times faster than manual slice tracing. Finally, we describe a computer-based tool and its application to 3D MDCT-based planning and follow-on live guidance of bronchoscopy. Conclusion The live-wire methods are efficient, reliable, easy to use, and applicable to a wide range of circumstances.

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Live-Wire Revisited

Cited by 8 publications

References 7 publications

Preoperative planning of a complete mastoidectomy: semiautomatic segmentation and evaluation

Preoperative planning of a complete mastoidectomy: semiautomatic segmentation and evaluation

Live-wire-based segmentation using similarities between corresponding image structures

Interactive segmentation based on the live wire for 3D CT chest image analysis

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