Real-time interactive three-dimensional segmentation

Saiviroonporn, Pairash; Robatino, Andre; Zahajszky, Janos; Kikinis, Ron; Jólesz, Ferenc A.

doi:10.1016/s1076-6332(98)80011-1

Cited by 23 publications

(15 citation statements)

References 9 publications

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“…Automatic or semiautomatic segmentation tools produce a rough representation of the anatomy that is manually edited to improve the results of the segmentation process (Udupa 1982, Kikinis et al 1996a, Saiviporoon et al 1998. Autocontouring and region growing tools are often used for semi-automatic volume definition (Drake et al 1991, Zimmer & Akselrod 1996, Saiviroonporn et al 1998. In some clinical cases, such as liver or airway segmentation from CT, fully automatic algorithms have been proposed (Bae et al Sonka et al 1994).…”

Section: User Processmentioning

confidence: 99%

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Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Alakuijala¹

2002

Medical Physics

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The use of image-guidance in surgery and radiotherapy has significantly improved patient outcome in neurosurgery and radiotherapy treatments. This work developed volume definition and verification techniques for image-guided applications, using a number of algorithms ranging from image processing to visualization.Stereoscopic visualization, volumetric tumor model overlaid on an ultrasound image, and visualization of the treatment geometry were experimented with on a neurosurgical workstation. Visualization and volume definition tools were developed for radiotherapy treatment planning system.The magnetic resonance inhomogeneity correction developed in this work, possibly the first published data-driven method with wide applicability, automatically mitigates the RF field inhomogeneity artefact present in magnetic resonance images. Correcting the RF inhomogeneity improves the accuracy of the generated volumetric models.Various techniques to improve region growing are also presented. The simplex search method and combinatory similarity terms were used to improve the similarity function with a low additional computational cost and high yield in region correctness. Moreover, the effects of different priority queue implementations were studied.A fast algorithm for calculating high-quality digitally reconstructed radiographs has been developed and shown to better meet typical radiotherapy needs than the two alternative algorithms. A novel visualization method, beam's light view, is presented. It uses texture mapping for projecting the fluence of a radiation field on an arbitrary surface.This work suggests several improved algorithms for image processing, segmentation, and visualization used in image-guided treatment systems. The presented algorithms increase the accuracy of image-guidance, which can further improve the applicability and efficiency of imageguided treatments.

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Section: User Processmentioning

confidence: 99%

“…Segmentation results are strongly dependent on the preprocessing algorithms. This obstructs direct comparison of different segmentation algorithms in commercial systems, in which preprocessing can be an integral part of the segmentation chain (Saiviroonporn et al 1998).…”

Section: Preprocessing Of Magnetic Resonance Imagesmentioning

confidence: 99%

Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Alakuijala¹

2002

Medical Physics

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“…These low-level functions commonly included morphological operations such as threshold, erode, dilate, and floodfill. For typical users of medical segmentation systems, this method has been shown to be intuitive and simple to learn [22,21]. Additionally, the user is given a sense of confidence about the result since he/she has been able to control it.…”

Section: D Segmentation Of Medical Datamentioning

confidence: 99%

“…More recently, Saiviroonporn et al [21] proposed a system for performing low-level segmentation operations on a CM-200 SIMD massively parallel processor. By utilizing a mesh of 128 2 PEs they were able to perform each low-level operation between 0.6 and 1.3 seconds per operation on a 256 3 volume.…”

Section: D Segmentation Of Medical Datamentioning

confidence: 99%

“…Both of these systems [11,21] showed the advantage of massively parallel processing and SIMD PEs for volume segmentation. By taking advantage of the high degree of data parallelism inherent in volume processing, these systems achieved faster segmentation than ever before; yet, it still is not within our definition of interactive speed.…”

Section: D Segmentation Of Medical Datamentioning

confidence: 99%

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Interactive volume segmentation with the PAVLOV architecture

Kreeger

Kaufman

1999

Proceedings of the 1999 IEEE Symposium on Parallel Visualization and Graphics - PVGS '99

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We propose a new paradigm for interactive volume visualization which allows enhanced exploration of datasets through the use of real-time volume processing (e.g., segmentation) tightly coupled with rendering. We show examples of how this paradigm supports a new way of examining a dataset by providing interactive visual feedback to segmentation parameter adjustment. We have developed a hardware co-processor, called PAVLOV, based on a SIMD mesh architecture. It supports our paradigm by providing volume processing at over 15Hz while also rendering of the results at 30Hz. We show how PAVLOV's programmability is used for volume processing applications, such as interactive segmentation and feature extraction. We further compare 3D segmentation on mesh architectures to known efficient methods for 2D segmentation.

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New paradigms for interactive 3D volume segmentation

Harders

Wildermuth²,

Székely³

2002

J. Visual. Comput. Animat.

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We present a new virtual reality-based interaction metaphor for semi-automatic segmentation of medical 3D volume data. The mouse-based, manual initialization of deformable surfaces in 3D represents a major bottleneck in interactive segmentation. In our multi-modal system we enhance this process with additional sensory feedback. A 3D haptic device is used to extract the centreline of a tubular structure. Based on the obtained path a cylinder with varying diameter is generated, which in turn is used as the initial guess for a deformable surface.

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Real-time interactive three-dimensional segmentation

Cited by 23 publications

References 9 publications

Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Interactive volume segmentation with the PAVLOV architecture

New paradigms for interactive 3D volume segmentation

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