Regional Appearance in Deformable Model Segmentation

Stough, Joshua V.; Broadhurst, Robert E.; Pizer, Stephen M.; Chaney, Edward L.

doi:10.1007/978-3-540-73273-0_44

Cited by 23 publications

(28 citation statements)

References 16 publications

(15 reference statements)

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“…Ignoring this negligible difference, our results were compared to those reported in Ref. 3. We find that our results are comparable, if with a slight decrease in accuracy.…”

Section: Iiib3 Comparison With the Other Methodssupporting

confidence: 67%

“…3, which has the best segmentation accuracy in literature to the best of our knowledge, our method was further evaluated on the same dataset as used in Ref. 3.…”

Section: Iiib3 Comparison With the Other Methodsmentioning

confidence: 99%

“…For our method, due to the fact that requirement of a planning image is required for training, there are 80−5=75 test images for evaluation in our experiment, instead of 80 test images in Ref. 3. Ignoring this negligible difference, our results were compared to those reported in Ref.…”

Section: Iiib3 Comparison With the Other Methodsmentioning

confidence: 99%

“…To avoid this problem, probability distribution functions ͑PDFs͒ of some chosen photometric variables taken over some regions of interest have been exploited for prostate segmentation. [1][2][3][4][5][6] These approaches are capable of detecting objects whose boundaries are not well-represented in terms of gradients, perhaps lacking sharp edges. Nonparametric probability distribution functions can be used to capture the prostate appearance variations.…”

Section: Ib Identification Of Optimal Image Features For Appearancementioning

confidence: 99%

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Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

Feng¹,

et al. 2010

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Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in imageguided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformablemodel-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function ͑PDF͒ features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation ͑from 94.1% to 99.3%͒. To evaluate the effectiveness of online learning mechanism, the authors carried out a comparison between partial online update strategy and full online update strategy. Using the full online update strategy, the mean DSC was improved from 86.6% to 89.3% with 2.8% gain. On the basis of full online update strategy, the manual modification before online update strategy was introduced and tested, the best performance was obtained; here, the mean DSC and the mean ASD achieved 92.4% and 1.47 mm, respectively. Conclusions: The proposed prostate segmentation method provided accurate and robust segmentation results for CT images even under the situation where the samples of patient under radiotherapy were limited. A conclusion that the proposed method is suitable for clinical application can be drawn.

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“…Ignoring this negligible difference, our results were compared to those reported in Ref. 3. We find that our results are comparable, if with a slight decrease in accuracy.…”

Section: Iiib3 Comparison With the Other Methodssupporting

confidence: 67%

“…3, which has the best segmentation accuracy in literature to the best of our knowledge, our method was further evaluated on the same dataset as used in Ref. 3.…”

Section: Iiib3 Comparison With the Other Methodsmentioning

confidence: 99%

Section: Iiib3 Comparison With the Other Methodsmentioning

confidence: 99%

Section: Ib Identification Of Optimal Image Features For Appearancementioning

confidence: 99%

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Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

Feng¹,

et al. 2010

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show abstract

“…Along these lines, previous studies have explored the use of medial representation for segmentation of anatomic structures. 23,[50][51][52][53] The incorporation of leaflet shape and appearance priors in such a framework could aid in identifying features like leaflet overlap and increase the robustness of segmentation in the presence of common ultrasound image artifacts.…”

Section: Ivb Challengesmentioning

confidence: 99%

Development of a semi-automated method for mitral valve modeling with medial axis representation using 3D ultrasound

Pouch¹,

Yushkevich²,

Jackson³

et al. 2012

Med. Phys.

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Purpose: Precise 3D modeling of the mitral valve has the potential to improve our understanding of valve morphology, particularly in the setting of mitral regurgitation (MR). Toward this goal, the authors have developed a user-initialized algorithm for reconstructing valve geometry from transesophageal 3D ultrasound (3D US) image data. Methods: Semi-automated image analysis was performed on transesophageal 3D US images obtained from 14 subjects with MR ranging from trace to severe. Image analysis of the mitral valve at midsystole had two stages: user-initialized segmentation and 3D deformable modeling with continuous medial representation (cm-rep). Semi-automated segmentation began with useridentification of valve location in 2D projection images generated from 3D US data. The mitral leaflets were then automatically segmented in 3D using the level set method. Second, a bileaflet deformable medial model was fitted to the binary valve segmentation by Bayesian optimization. The resulting cm-rep provided a visual reconstruction of the mitral valve, from which localized measurements of valve morphology were automatically derived. The features extracted from the fitted cm-rep included annular area, annular circumference, annular height, intercommissural width, septolateral length, total tenting volume, and percent anterior tenting volume. These measurements were compared to those obtained by expert manual tracing. Regurgitant orifice area (ROA) measurements were compared to qualitative assessments of MR severity. The accuracy of valve shape representation with cm-rep was evaluated in terms of the Dice overlap between the fitted cm-rep and its target segmentation. Results: The morphological features and anatomic ROA derived from semi-automated image analysis were consistent with manual tracing of 3D US image data and with qualitative assessments of MR severity made on clinical radiology. The fitted cm-reps accurately captured valve shape and demonstrated patient-specific differences in valve morphology among subjects with varying degrees of MR severity. Minimal variation in the Dice overlap and morphological measurements was observed when different cm-rep templates were used to initialize model fitting. Conclusions: This study demonstrates the use of deformable medial modeling for semi-automated 3D reconstruction of mitral valve geometry using transesophageal 3D US. The proposed algorithm provides a parametric geometrical representation of the mitral leaflets, which can be used to evaluate valve morphology in clinical ultrasound images.

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A Learning Based Hierarchical Framework for Automatic Prostate Localization in CT Images

Liao

Shen

2011

Lecture Notes in Computer Science

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Accurate localization of prostate in CT images plays an important role in image guided radiation therapy. However, it has two main challenges. The first challenge is due to the low contrast in CT images. The other challenge is due to the uncertainty of the existence of bowel gas. In this paper, a learning based hierarchical framework is proposed to address these two challenges. The main contributions of the proposed framework lie in the following aspects: (1) Anatomical features are extracted from input images, and the most salient features at distinctive image regions are selected to localize the prostate. Regions with salient features but irrelevant to prostate localization are also filtered out. (2) An image similarity measure function is explicitly defined and learnt to enforce the consistency between the distance of the learnt features and the underlying prostate alignment. (3) An online learning mechanism is used to adaptively integrate both the inter-patient and patient-specific information to localize the prostate. Based on the learnt image similarity measure function, the planning image of the underlying patient is aligned to the new treatment image for segmentation. The proposed method is evaluated on 163 3D prostate CT images of 10 patients, and promising experimental results are obtained.

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Regional Appearance in Deformable Model Segmentation

Cited by 23 publications

References 16 publications

Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

Development of a semi-automated method for mitral valve modeling with medial axis representation using 3D ultrasound

A Learning Based Hierarchical Framework for Automatic Prostate Localization in CT Images

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