Evaluation of multi-atlas-based segmentation of CT scans in prostate cancer radiotherapy

Acosta, Oscar; Simon, Antoine; Monge, Frédéric; Commandeur, Frédéric; Bassirou, Cunka; Cazoulat, Guillaume; Crevoisier, R. de; Haigron, Pascal

doi:10.1109/isbi.2011.5872795

Cited by 36 publications

(43 citation statements)

References 12 publications

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“…In this section, we evaluated several multi-atlas based strategies, as an extension of [52], taking into account the different stages of the pipeline depicted in Figure 12.i) selection of the atlases based on three different metrics: sum of squared differences, cross correlation and mutual information;…”

Section: Evaluation Of Multi-atlas Based Segmentationmentioning

confidence: 99%

Multi-Atlas-Based Segmentation of Pelvic Structures from CT Scans for Planning in Prostate Cancer Radiotherapy

Acosta

Dowling

Dréan

et al. 2013

Abdomen and Thoracic Imaging

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In prostate cancer radiotherapy, the accurate identification of the prostate and organs at risk in planning computer tomography (CT) images is an important part of the therapy planning and optimization. Manually contouring these organs can be a time consuming process and subject to intra-and inter-expert variability. Automatic identification of organ boundaries from these images is challenging due to the poor soft tissue contrast. Atlas-based approaches may provide a priori structural information by propagating manual expert delineations to a new individual space; however the inter-individual variability and registration errors may lead to biased results. Multiatlas approaches can partly overcome some of these difficulties by selecting the most similar atlases among a large data base but the definition of similarity measure between the available atlases and the query individual has still to be addressed. The purpose of this chapter is to explain atlas-based segmentation approaches and the evaluation of different atlas-based strategies to simultaneously segment prostate, bladder and rectum from CT images. A comparison between single and multiple atlases is performed. Experiments on atlas ranking, selection strategies and fusion decision rules are carried out to illustrate the presented methodology. Propagation of labels using two registration strategies are applied and the results of the comparison with manual delineations are reported.

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Section: Evaluation Of Multi-atlas Based Segmentationmentioning

confidence: 99%

Multi-Atlas-Based Segmentation of Pelvic Structures from CT Scans for Planning in Prostate Cancer Radiotherapy

Acosta

Dowling

Dréan

et al. 2013

Abdomen and Thoracic Imaging

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“…Different decision rules may be applied, such as a simple voting-rule [34], a weighted decision based on similarity [35] or a Bayesian approach, such as the simultaneous truth and performance level estimation (STAPLE) [28]. We opted for the weighted-label fusion approach, resulting in the following probability map: Thus, the contribution of each non-rigidly propagated label to this map heavily depended on the similarity between the atlas and the query image.…”

Section: Weighted-label Fusion (Step 6)mentioning

confidence: 99%

Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy

Acosta

Mylona

Dain

et al. 2017

Radiotherapy and Oncology

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“…The first involves a single atlas [120], while in the second, multiple atlases are considered. This multi-atlas approach [121][122][123][124][125][126] implies successive registrations of the target image with the image atlases. A score based on a similarity measure is associated with each registration, and the atlas image with the best score serves as a reference to segment the target image.…”

Section: 123mentioning

confidence: 99%

“…Feng et al [49] Shape and appearance AAM with ACP Acosta et al [123] Multi-atlas Ghanei et al [26] Shape SCDM with statistical Yu et al [52] Appearance Statistical Hwee et al [120] Multi-atlas Li et al [15] Intensity and context Statistical Pasquier et al [84] Shape ASM with ACP Predictive modeling of prostate motion Boubaker et al [136] Tissue motion and deformation Biomechanical…”

Section: Biopsy Registrationmentioning

confidence: 99%

“…In both steps, imaging is used [23,49,102,123]. In the first step, CT and MR images are used to delineate the prostate and some organs at risk, such as the bladder and rectum, while in the second step, the challenge is to manage the dose delivery by returning the patient to his planning position and by taking into account motions and deformations [136].…”

Section: Radiotherapymentioning

confidence: 99%

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A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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Computer technology is widely used for multimodal image analysis of the prostate gland. Several techniques have been developed, most of which incorporate a priori knowledge extracted from organ features. Knowledge extraction and modeling are multi-step tasks. Here, we review these steps and classify the modeling according to the data analysis methods employed and the features used. We conclude with a survey of some clinical applications where these techniques are employed.

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Evaluation of multi-atlas-based segmentation of CT scans in prostate cancer radiotherapy

Cited by 36 publications

References 12 publications

Multi-Atlas-Based Segmentation of Pelvic Structures from CT Scans for Planning in Prostate Cancer Radiotherapy

Multi-Atlas-Based Segmentation of Pelvic Structures from CT Scans for Planning in Prostate Cancer Radiotherapy

Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy

A survey of prostate modeling for image analysis

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