Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

Gorthi, Subrahmanyam; Duay, Valérie; Houhou, Nawal; Cuadra, M. Bach; Schick, Ulrike; Becker, Minerva; Allal, Abdelkarim Said; Thiran, Jean-Philippe

doi:10.1109/jstsp.2008.2011104

Cited by 52 publications

(49 citation statements)

References 27 publications

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“…In the work of Gorthi et al 4 CT images are of similar size ͑0.9375 mmϫ 0.9375 mmϫ 3 mm͒ and the sensitivity, DSC, and Hausdorff distance are reported. In their leave-one-out experiment, the mean DSC reported for levels IIA, IIB, III, and IV are 0.53, 0.46, 0.43, and 0.36, respectively, while the mean DSC for our ASM-based segmentation is 0.698 for levels II-IV segmented as a single structure.…”

Section: Discussionmentioning

confidence: 99%

“…The quantitative validation performed in this study showed an improvement in specificity compared to a standard atlas-based method as well as a reduction in sensitivity. Gorthi et al 4 also used an atlas-based approach, but the deformation was computed with structures easily visible in the images ͑bones, trachea, and skin͒ and then applied to the rest of the image. This led to relatively large ͑14.52-21.81 mm͒ segmentation errors for the average Hausdorff distance for node levels II-IV.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Many works about lymph node segmentation in CT images have been developed. [33][34][35][36][37][38][39][40][41] In this work, using the centroids of lymph node candidates as seed points [ Fig. 8(a) a level-set-based curve evolution is applied [ Fig.…”

Section: D False Positive Rejectionmentioning

confidence: 99%

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

et al. 2016

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Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations. [http://dx.doi.org/10.1118/1.4954009]

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“…The ground truth segmentations are performed by a medical expert, under the supervision of a radiation oncologist. In Gorthi et al (2009), we used a preliminary version of the current framework with the main focus being the specific H&N lymph nodes segmentation problem. We now perform a detailed evaluation with the current framework, and also study the effect of combining various registration forces in an hierarchical manner for this application.…”

Section: Segmentation Of Head and Neck Lymph Nodesmentioning

confidence: 99%

Active deformation fields: Dense deformation field estimation for atlas-based segmentation using the active contour framework

Gorthi

Duay

Bresson³

et al. 2011

Medical Image Analysis

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a b s t r a c tThis paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.

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Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

Cited by 52 publications

References 27 publications

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

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

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

Active deformation fields: Dense deformation field estimation for atlas-based segmentation using the active contour framework

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