Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

Shimizu, A.; Kimoto, Tsunenobu; Kobatake, Hidefumi; Nakamura, Shigeru; Shinozaki, K.

doi:10.1007/s11548-009-0384-0

Cited by 75 publications

(70 citation statements)

References 12 publications

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“…The liver enjoyed special attention in recent literature (Delingette and Ayache, 2005; Heimann et al, 2009; Linguraru et al, 2010; Okada et al, 2008a; Soler et al 2001; Song et al, 2009; Wimmer et al, 2009), kidneys were analyzed sporadically (Ali et al, 2007; Shim et al, 2009; So and Chung, 2009), while the spleen (Danelon and Stitzel, 2008; Linguraru et al, 2010) and pancreas (Shimizu et al, 2010a) were segmented less frequently. Model driven approaches have been both popular and successful (Soler et al 2001; Song et al, 2009), including active and statistical shape models (Okada et al, 2008a; So and Chung, 2009; Wimmer et al, 2009) and atlas-based segmentation (Linguraru et al, 2010; Okada et al, 2008a; Shimizu et al, 2010a). Level sets and geodesic active contours were frequently involved in these techniques (Heimann et al, 2009; Linguraru et al, 2010; Wimmer et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT

Linguraru

Pura

Pamulapati

et al. 2012

Medical Image Analysis

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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts. Contrast-enhanced CT scans were obtained at two phases: non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration. Then 4D convolution using population training information of contrast-enhanced liver, spleen and kidneys was applied to multiphase data to initialize the 4D graph and adapt to patient-specific data. CT enhancement information and constraints on shape, from Parzen windows, and location, from a probabilistic atlas, were input into a new formulation of a 4D graph. Comparative results demonstrate the effects of appearance, enhancement, shape and location on organ segmentation. All four abdominal organs were segmented robustly and accurately with volume overlaps over 93.6% and average surface distances below 1.1 mm.

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Section: Introductionmentioning

confidence: 99%

Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT

Linguraru

Pura

Pamulapati

et al. 2012

Medical Image Analysis

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“…Some specific approaches for single organ segmentation, e.g., liver (Heimann et al, 2009) and pancreas (Shimizu et al, 2010), can provide higher performances, while our efforts in this study focus on the development of a generic approach for multiple organ segmentation. In addition, provided with adequate number (>20) of labeled atlases, we expect that our proposed method can be adapted to other thoracic (e.g., lungs), abdominal (e.g., psoas muscles), and pelvic (e.g., prostate) organs on CT, where the organs to segment have (1) consistent intensity-based and spatial appearance, (2) high contrast to the surrounding tissues, and (3) reasonable amount of overlap between the registered atlases and the target.…”

Section: Discussionmentioning

confidence: 99%

Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning

Burke

Lee

et al. 2015

Medical Image Analysis

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Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.

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“…The relationship of SSM and PA is not extensively studied in existing references of human abdominal CT. Okada et al [62], [63] combined SSM and PA for the segmentation of human abdominal organs, but SSM and PA are constructed and applied separately. Shimizu et al [64] correlated pancreas PA with the SSM of pancreas centerline, but each instance of the SSM had to be endowed with a different PA, making the optimization of individualized PA computationally expensive; hence they used only a limited number of pre-sampled SSM instances for the optimization. In this paper, since the conditional distribution of SSM L is already individualized for the specific subject, it is straightforward to generate an individualized PA from SSM L .…”

Section: Discussionmentioning

confidence: 99%

Estimation of Mouse Organ Locations Through Registration of a Statistical Mouse Atlas With Micro-CT Images

Wang

Stout²,

Chatziioannou³

2012

IEEE Trans. Med. Imaging

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Micro-CT is widely used in preclinical studies of small animals. Due to the low soft-tissue contrast in typical studies, segmentation of soft tissue organs from noncontrast enhanced micro-CT images is a challenging problem. Here, we propose an atlas-based approach for estimating the major organs in mouse micro-CT images. A statistical atlas of major trunk organs was constructed based on 45 training subjects. The statistical shape model technique was used to include inter-subject anatomical variations. The shape correlations between different organs were described using a conditional Gaussian model. For registration, first the high-contrast organs in micro-CT images were registered by fitting the statistical shape model, while the low-contrast organs were subsequently estimated from the high-contrast organs using the conditional Gaussian model. The registration accuracy was validated based on 23 noncontrast-enhanced and 45 contrast-enhanced micro-CT images. Three different accuracy metrics (Dice coefficient, organ volume recovery coefficient, and surface distance) were used for evaluation. The Dice coefficients vary from 0.45 ± 0.18 for the spleen to 0.90 ± 0.02 for the lungs, the volume recovery coefficients vary from for the liver to 1.30 ± 0.75 for the spleen, the surface distances vary from 0.18 ± 0.01 mm for the lungs to 0.72 ± 0.42 mm for the spleen. The registration accuracy of the statistical atlas was compared with two publicly available single-subject mouse atlases, i.e., the MOBY phantom and the DIGIMOUSE atlas, and the results proved that the statistical atlas is more accurate than the single atlases. To evaluate the influence of the training subject size, different numbers of training subjects were used for atlas construction and registration. The results showed an improvement of the registration accuracy when more training subjects were used for the atlas construction. The statistical atlas-based registration was also compared with the thin-plate spline based deformable registration, commonly used in mouse atlas registration. The results revealed that the statistical atlas has the advantage of improving the estimation of low-contrast organs.

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Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

Abstract: This study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.

Cited by 75 publications

References 12 publications

Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT

Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT

Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning

Estimation of Mouse Organ Locations Through Registration of a Statistical Mouse Atlas With Micro-CT Images

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