Efficient Selection of the Most Similar Image in a Database for Critical Structures Segmentation

Commowick, Olivier; Malandain, Grégoire

doi:10.1007/978-3-540-75759-7_25

Cited by 24 publications

(30 citation statements)

References 11 publications

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“…Alternatively, atlas ranking can be based on local deformations [8,5,9]. In this case, the most similar atlas is the one that requires the smallest local deformation to be matched on the patient.…”

Section: Deformation-based Ranking Methodsmentioning

confidence: 99%

“…In this case, the most similar atlas is the one that requires the smallest local deformation to be matched on the patient. As in [5], we estimate these local deformations through the intermediate referential of the average image.…”

Section: Deformation-based Ranking Methodsmentioning

confidence: 99%

“…The potential atlases can be average atlases precomputed from homogeneous sub-groups of the database that can possibly be obtained by atlas stratification [3]. Alternatively, each image of the database can be considered as a potential atlas [4,5]. By extension, multi-atlas based segmentation consists in selecting the most appropriate images among the database (atlas selection step) and fusing their segmentations, either globally [6,7,8] or locally [9] (atlas fusion step).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, several image-based selection methods have been proposed. The selection can be based on similarity measures between the patient image and each potential atlas [6,7,8,4] or based on local deformations [8,5,9]. Therefore, the comparison of these different methods for a given application is of interest.…”

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: Deformation-based Ranking Methodsmentioning

confidence: 99%

Section: Deformation-based Ranking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…The similarity can be also based on information obtained from the deformation [53], such as the Jacobian. The drawback of the Jacobian is the dependency on the registration method used to align the images.…”

Section: Multi-atlas Strategy: Selecting the N Best Atlas From A Datamentioning

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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Deep learning‐based auto‐segmentation of clinical target volumes for radiotherapy treatment of cervical cancer

Zhou

Guo

et al. 2021

J Applied Clin Med Phys

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Objectives Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)‐based auto‐segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers. Methods Computed tomography (CT) datasets from 535 cervical cancers treated with definitive or postoperative radiotherapy were collected. A DL tool based on VB‐Net was developed to delineate CTVs of the pelvic lymph drainage area (dCTV1) and parametrial area (dCTV2) in the definitive radiotherapy group. The training/validation/test number is 157/20/23. CTV of the pelvic lymph drainage area (pCTV1) was delineated in the postoperative radiotherapy group. The training/validation/test number is 272/30/33. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) were used to evaluate the contouring accuracy. Contouring times were recorded for efficiency comparison. Results The mean DSC, MSD, and HD values for our DL‐based tool were 0.88/1.32 mm/21.60 mm for dCTV1, 0.70/2.42 mm/22.44 mm for dCTV2, and 0.86/1.15 mm/20.78 mm for pCTV1. Only minor modifications were needed for 63.5% of auto‐segmentations to meet the clinical requirements. The contouring accuracy of the DL‐based tool was comparable to that of senior radiation oncologists and was superior to that of junior/intermediate radiation oncologists. Additionally, DL assistance improved the performance of junior radiation oncologists for dCTV2 and pCTV1 contouring (mean DSC increases: 0.20 for dCTV2, 0.03 for pCTV1; mean contouring time decrease: 9.8 min for dCTV2, 28.9 min for pCTV1). Conclusions DL‐based auto‐segmentation improves CTV contouring accuracy, reduces contouring time, and improves clinical efficiency for treating cervical cancer.

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Efficient Selection of the Most Similar Image in a Database for Critical Structures Segmentation

Cited by 24 publications

References 11 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

Deep learning‐based auto‐segmentation of clinical target volumes for radiotherapy treatment of cervical cancer

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