Aorta segmentation using the watershed algorithm for an augmented reality system in laparoscopic surgery

López-Mir, Fernando; Naranjo, Valery; Angulo, Jesús; Villanueva, Eliseo P.; Raya, Mariano Alcañiz; López-Celada, Susana

doi:10.1109/icip.2011.6116211

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…Watershed methods (WS) use variants of the classical algorithm in [62]. Method WS b uses viscosity and markers to overcome the local minima problems in image gradient [63]. The thresholding methods were not among the top ranking ones.…”

Section: Resultsmentioning

confidence: 99%

Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information

Song

Bai

Han

et al. 2013

IEEE Trans. Med. Imaging

123

146

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PET-CT images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov Random Field (MRF) model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two subgraphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT.

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Section: Resultsmentioning

confidence: 99%

Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information

Song

Bai

Han

et al. 2013

IEEE Trans. Med. Imaging

123

146

View full text Add to dashboard Cite

show abstract

“…Neither the method nor the user were specialized in medical imaging. Method WS b , similar way to that in [41], uses two procedures to overcome problems associated with local minima in image gradient. First, viscosity is added to the watershed, which closes gaps in the edge-map.…”

Section: Section II Contouring Methodsmentioning

confidence: 99%

Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy

Shepherd

Teräs

Beichel

et al. 2012

IEEE Trans. Med. Imaging

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The impact of positron emission tomography (PET) on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the ill-definition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasize spatial accuracy and present a new metric that addresses the lack of unique ground truth. Thirty methods are used at 13 different institutions to contour functional volumes of interest in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualization of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm.

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“…When MR images are acquired, the patient must lie on a stretcher with his/her back straight and centered on both sides to calculate the position and the orientation relative to an initial coordinate system. A virtual model of the patient's organs is extracted from these images using techniques of digital image processing, especially our own image segmentation algorithms [ 28 ] and others developed in [ 29 ]. With this model, the clinician selects the patient's navel in the MRI images to establish the origin of 3D space at that point in order to perform the registration with the real-time image ( 4 ).…”

Section: Methodsmentioning

confidence: 99%

Design and Validation of an Augmented Reality System for Laparoscopic Surgery in a Real Environment

López-Mir

Naranjo

Fuertes

et al. 2013

BioMed Research International

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Purpose. This work presents the protocol carried out in the development and validation of an augmented reality system which was installed in an operating theatre to help surgeons with trocar placement during laparoscopic surgery. The purpose of this validation is to demonstrate the improvements that this system can provide to the field of medicine, particularly surgery. Method. Two experiments that were noninvasive for both the patient and the surgeon were designed. In one of these experiments the augmented reality system was used, the other one was the control experiment, and the system was not used. The type of operation selected for all cases was a cholecystectomy due to the low degree of complexity and complications before, during, and after the surgery. The technique used in the placement of trocars was the French technique, but the results can be extrapolated to any other technique and operation. Results and Conclusion. Four clinicians and ninety-six measurements obtained of twenty-four patients (randomly assigned in each experiment) were involved in these experiments. The final results show an improvement in accuracy and variability of 33% and 63%, respectively, in comparison to traditional methods, demonstrating that the use of an augmented reality system offers advantages for trocar placement in laparoscopic surgery.

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Aorta segmentation using the watershed algorithm for an augmented reality system in laparoscopic surgery

Cited by 5 publications

References 7 publications

Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information

Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information

Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy

Design and Validation of an Augmented Reality System for Laparoscopic Surgery in a Real Environment

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