Geometrical model-based segmentation of the organs of sight on CT images

Bekes, György; Máté, Eörs; Nyúl, László G.; Kuba, Attila; Fidrich, Márta

doi:10.1118/1.2826557

Cited by 37 publications

(43 citation statements)

References 6 publications

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“…Parametric deformable models Lee [41] Brainstem, cerebellum MR McIntosh [42] Corpus callosum MR Szekely [57] Multi-structure MR McInerney [92] Corpus callosum, cerebellum MR Geometric deformable models Shen [24] Hippocampus MR T1 Zhao [27] Hippocampus MR Ghanei [37] Hippocampus MR Leventon [43] Corpus callosum MR Yang [58] Multi-structure MR Tsai [59] Ventricle, caudate nuclei, lenticular nucleus MR Wang [94] Corpus callosum, basal ganglia, ventricle boundaries MR Duncan [95] Hippocampus MR T1 Bekes [96] Eyeballs, lens, nerves CT Machine learning. ANN Hult [25] Hippocampus MR T1, T2 Magnotta [60] Corpus callosum, putamen, caudate nuclei MR T1, T2 Powell [61] Multi-structure MR T1, T2, PD Pierson [62] Cerebellar subregions MR T1, T2 Spinks [99] Thalamus, mediodorsal nucleus MR T1, T2, PD Moghaddam [100] Putamen, caudate, thalamus MR T1…”

Section: Structures Image Modalitiesmentioning

confidence: 99%

“…According to the type of shape representation used to define the model, DM methods can be categorized in: parametric or explicit deformable models [23,41,42,57,91,92] and geometric or implicit deformable models [27,37,43,58,59,[93][94][95][96][97][98].…”

Section: Deformable Modelsmentioning

confidence: 99%

“…Mesejo et al [98] presented a segmentation approach based on the LS method, called HybridLS, that combined edge, region and prior shape knowledge of the target object to guide the LS evolution. Because of these advantages geometric DMs have been extensively used to carry out the segmentation task of brain subcortical structures [37,43,58,59,[94][95][96][97][98].…”

Section: Geometric Deformable Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Dolz

Massoptier²,

Vermandel

2015

IRBM

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This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery.

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Section: Structures Image Modalitiesmentioning

confidence: 99%

Section: Deformable Modelsmentioning

confidence: 99%

Section: Geometric Deformable Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Dolz

Massoptier²,

Vermandel

2015

IRBM

View full text Add to dashboard Cite

show abstract

“…Bekes et al . (4) proposed a geometric model-based method for semi-automatic segmentation of the eye balls, lenses, optic nerves and optic chiasm in CT images and reported quantitative sensitivity and specific results from SRAPLE (5) of approximately 77% and 94% for the chiasm. Qualitatively, Bekes et al reported a lack of consistency among human raters and with the results they obtain for the nerves and chiasm.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of multiatlas label fusion forin vivomagnetic resonance imaging orbital segmentation

et al. 2014

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Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. We evaluate 7 statistical and voting-based label fusion algorithms (and 6 additional variants) to segment the optic nerves, eye globes and chiasm. For non-local STAPLE, we evaluate different intensity similarity measures (including mean square difference, locally normalized cross correlation, and a hybrid approach). Each algorithm is evaluated in terms of the Dice overlap and symmetric surface distance metrics. Finally, we evaluate refinement of label fusion results using a learning based correction method for consistent bias correction and Markov random field regularization. The multi-atlas labeling pipelines were evaluated on a cohort of 35 subjects including both healthy controls and patients. Across all three structures, NLSS with a mixed weighting type provided the most consistent results; for the optic nerve NLSS resulted in a median Dice similarity coefficient of 0.81, mean surface distance of 0.41 mm and Hausdorff distance 2.18 mm for the optic nerves. Joint label fusion resulted in slightly superior median performance for the optic nerves (0.82, 0.39 mm and 2.15 mm), but slightly worse on the globes. The fully automated multi-atlas labeling approach provides robust segmentations of orbital structures on MRI even in patients for whom significant atrophy (optic nerve head drusen) or inflammation (multiple sclerosis) is present.

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“…However, current segmentation procedures often require manual intervention due to anatomical and imaging variability. Bekes et al 4 proposed a geometric model-based method for semiautomatic segmentation of the eye balls, lenses, ONs, and optic chiasm in computed tomography (CT) images and reported quantitative sensitivity and specificity results from simultaneous truth and performance level estimation (STAPLE) 5 of ∼77%. Qualitatively, this study reported a lack of consistency with the results they obtain for the nerves and chiasm.…”

Section: Introductionmentioning

confidence: 99%

Robust optic nerve segmentation on clinically acquired computed tomography

et al. 2014

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Abstract. The optic nerve (ON) plays a critical role in many devastating pathological conditions. Segmentation of the ON has the ability to provide understanding of anatomical development and progression of diseases of the ON. Recently, methods have been proposed to segment the ON but progress toward full automation has been limited. We optimize registration and fusion methods for a new multi-atlas framework for automated segmentation of the ONs, eye globes, and muscles on clinically acquired computed tomography (CT) data. Briefly, the multi-atlas approach consists of determining a region of interest within each scan using affine registration, followed by nonrigid registration on reduced field of view atlases, and performing statistical fusion on the results. We evaluate the robustness of the approach by segmenting the ON structure in 501 clinically acquired CT scan volumes obtained from 183 subjects from a thyroid eye disease patient population. A subset of 30 scan volumes was manually labeled to assess accuracy and guide method choice. Of the 18 compared methods, the ANTS Symmetric Normalization registration and nonlocal spatial simultaneous truth and performance level estimation statistical fusion resulted in the best overall performance, resulting in a median Dice similarity coefficient of 0.77, which is comparable with inter-rater (human) reproducibility at 0.73.

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Geometrical model-based segmentation of the organs of sight on CT images

Cited by 37 publications

References 6 publications

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Evaluation of multiatlas label fusion forin vivomagnetic resonance imaging orbital segmentation

Robust optic nerve segmentation on clinically acquired computed tomography

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