Atlas-based delineation of lymph node levels in head and neck computed tomography images

Commowick, Olivier; Grégoire, Vincent; Malandain, Grégoire

doi:10.1016/j.radonc.2008.01.018

Cited by 92 publications

(86 citation statements)

References 32 publications

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“…In the non-linear case, we chose to deform the potential atlases and the patient image onto the average image pre-computed from the N − 1 remaining patients using [1], and to compute the similarity measures in this referential. This is computationally interesting because the nonlinear deformation between each potential atlas and the average image has already been computed during the construction of the average image.…”

Section: Intensity-based Ranking Methodsmentioning

confidence: 99%

“…These delineations are traditionally made manually by experts but this task is tedious and not reproducible. Atlas-based segmentation has proved to be an efficient procedure to automatically get these delineations for the head and neck region [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…When the atlas used is a particular segmented patient image, it may be very similar to some patients, but very different from others. An average atlas built from a database of several segmented images provides better results [1] , but it still has difficulties to cope with very high anatomical variabilities as those in the head and neck region.…”

Section: Introductionmentioning

confidence: 99%

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Assessing selection methods in the context of multi-atlas based segmentation

Ramus

Malandain

2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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In atlas-based segmentation, using one single atlas for segmenting all patients introduces a bias. Multi-atlas techniques overcome this drawback by selecting and fusing the most appropriate atlases among a database for a given patient. Globally assessing different multi-atlas strategies provides a biased evaluation of the atlas selection methods. To address this problem, we propose to evaluate atlas selection methods independently from the number of atlases selected and from the atlas fusion step. Briefly, we first cluster the selection methods on the basis of rank correlation and then assess each sub-group of methods with respect to a sub-group of reference selection methods. We apply our method to 105 images of the head and neck region.

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Section: Intensity-based Ranking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing selection methods in the context of multi-atlas based segmentation

Ramus

Malandain

2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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“…In atlas-based methods a pre-computed segmentation or prior information in a template space is propagated towards the image to be segmented via spatial normalization (registration). These methods have been largely used in brain MRI ( [26], [27] ), head and neck CT Scans ( [28], [29], [30] ), cardiac aortic CT [31], pulmonary lobes from CT [32] and prostate MR ( [33], [34] ) . In the atlas based methods image registration is a key element, as label propagation relies on the registration of one or more templates to a target image.…”

Section: Introductionmentioning

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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“…Instead of using these automatically generated contours directly, they presented contours to physicians for modification and then compared these edited contours to manual delineations. In another study, Commowick et al 2 projected lymph node contours from an average image volume to patient CT images using global affine and local nonrigid transformations. Although the volume-based error measure showed that, overall, the atlas-based delineations were acceptable, oversegmentations of the lymph node regions were observed.…”

Section: Introductionmentioning

confidence: 99%

Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

Chen

Deeley²,

Niermann³

et al. 2010

Medical Physics

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Purpose: Intensity-modulated radiation therapy ͑IMRT͒ is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model ͑ASM͒ approach. Methods: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution. Results: The method was evaluated through a leave-one-out experiment. The ASM-and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient ͑DSC͒ for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively. Conclusions: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.

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Atlas-based delineation of lymph node levels in head and neck computed tomography images

Cited by 92 publications

References 32 publications

Assessing selection methods in the context of multi-atlas based segmentation

Assessing selection methods in the context of multi-atlas based segmentation

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

Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

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