Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context

Isambert, A.; Dhermain, Frédéric; Bidault, François; Commowick, Olivier; Bondiau, Pierre‐Yves; Malandain, Grégoire; Lefkopoulos, Dimitri

doi:10.1016/j.radonc.2007.11.030

Cited by 141 publications

(149 citation statements)

References 20 publications

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“…As we are dealing with binary structures, the number of true negatives is generally much larger than false positives, thus leading to non-meaningful specificity values, as reported also by Isambert et al (35). We therefore decided to synthetize the results in terms of Positive Predicted Value (PPV), which is defined as ϭ ϩ PPV TP TP FP [3] and quantifies the rate of correctly propagated voxels with respect to the total number of voxels in the propagated volume.…”

Section: Methodsmentioning

confidence: 99%

“…We evaluated the performance of Volume Difference accuracy (VD), Dice Similarity Coefficient (DSC) (34), Receiver Operating Characteristic (ROC) (35,36) and Surface Distance (SD) (29,30 [2] and represents the ratio between the amount of overlap of two structures and the mean total volume. It ranges from 0 to 1, with 1 a perfect overlap between the examined structures.…”

Section: Metricsmentioning

confidence: 99%

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Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

Peroni

Spadea

Riboldi

et al. 2013

Technol Cancer Res Treat

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The aim of this work is to provide insights into multiple metrics clinical validation of deformable image registration and contour propagation methods in 4D lung radiotherapy planning. The following indices were analyzed and compared: Volume Difference (VD), Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV) and Surface Distances (SD). The analysis was performed on three patient datasets, using as reference a ground-truth volume generated by means of Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm from the outlines of five experts. Significant discrepancies in the quality assessment provided by the different metrics in all the examined cases were found. Metrics sensitivity was more evident in presence of image artifacts and particularly for tubular anatomical structures, such as esophagus or spinal cord. Volume Differences did not account for position and DSC exhibited criticalities due to its intrinsic symmetry (i.e. over-and under-estimation of the reference contours cannot be discriminated) and dependency on the total volume of the structure. PPV analysis showed more robust performance, as each voxel concurs to the classification of the propagation, but was not able to detect inclusion of propagated and ground-truth volumes.Mesh distances could interpret the actual shape of the structures, but might report higher mismatches in case of large local differences in the contour surfaces. According to our study, the combination of VD and SD for the validation of contour propagation algorithms in 4D could provide the necessary failure detection accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

Peroni

Spadea

Riboldi

et al. 2013

Technol Cancer Res Treat

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“…These can be made arbitrarily large, thus leading to large specificity values and requiring heuristic criteria to define TN, as discussed in Ref. 16.…”

Section: Discussionmentioning

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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“…Along with the two different kinds of data sets available, the current segmentation methods can also be divided into two different categories: In most cases, classification methods with some degree of spatial regularization are employed for the segmentation of the multimodal dataset (Bauer, Nolte, & Reyes, 2011;Verma et al, 2008;Wels et al, 2008), while atlas-based segmentation is an established way for segmenting the mono-modal image (Bach Cuadra et al, 2006;Bauer, May, et al, 2012;Deeley et al, 2011;Gooya, Pohl, Bilello, Biros, & Davatzikos, 2011;Isambert et al, 2008;Zacharaki, Hogea, Shen, Biros, & Davatzikos, 2009). Using atlas-based segmentation on the high-resolution mono-modal image is attractive thanks to its robustness and its versatile usability because it allows DTI maps or subcortical label maps to be overlaid on the patient image, which is important information in neurosurgery and radiotherapy.…”

Section: Figurementioning

confidence: 99%

Integrated segmentation of brain tumor images for radiotherapy and neurosurgery

Bauer

May

et al. 2013

Int J Imaging Syst Tech

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Segmentation of brain tumor images is an important task in diagnosis and treatment planning for cancer patients. To achieve this goal with standard clinical acquisition protocols, conventionally, either classification algorithms are applied on multimodal MR images or atlas-based segmentation is used on a high-resolution mono-modal MR image. These two approaches have been commonly regarded separately. We propose to integrate all the available imaging information into one framework in order to be able to use the information gained from the tissue classification of the multimodal images to perform a more precise segmentation on the highresolution mono-modal image by atlas-based segmentation. For this, we combine a state of the art regularized classification method with an enhanced version of an atlas-registration approach including multi-scale tumor-growth modeling. This contribution offers the possibility to simultaneously segment subcortical structures in the patient by warping the respective atlas labels, which is important for neurosurgical planning and radiotherapy planning.

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Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context

Cited by 141 publications

References 20 publications

Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

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

Integrated segmentation of brain tumor images for radiotherapy and neurosurgery

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