Simultaneous segmentation of multi-organ in three dimensional abdominal CT images

Shimizu, A.; Ikegami, T.; Yanagita, Tomohiko; Kobatake, H.; Nakamura, Shigeru

doi:10.1016/j.ics.2005.03.097

Cited by 17 publications

(16 citation statements)

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“…Although atlas-based methods have enjoyed much popularity [3,15,16] their conceptual simplicity is in contrast to the need for robust, cross-patient registration. Robustness is improved by multi-atlas techniques [17], at the price of slower algorithms involving multiple registrations.…”

Section: Introductionmentioning

confidence: 99%

Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

Criminisi

Shotton

Robertson

et al. 2011

Lecture Notes in Computer Science

202

159

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Abstract. This paper proposes multi-class random regression forests as an algorithm for the efficient, automatic detection and localization of anatomical structures within three-dimensional CT scans.Regression forests are similar to the more popular classification forests, but trained to predict continuous outputs. We introduce a new, continuous parametrization of the anatomy localization task which is effectively addressed by regression forests. This is shown to be a more natural approach than classification.A single pass of our probabilistic algorithm enables the direct mapping from voxels to organ location and size; with training focusing on maximizing the confidence of output predictions. As a by-product, our method produces salient anatomical landmarks; i.e. automatically selected "anchor" regions which help localize organs of interest with high confidence. Quantitative validation is performed on a database of 100 highly variable CT scans. Localization errors are shown to be lower (and more stable) than those from global affine registration approaches. The regressor's parallelism and the simplicity of its context-rich visual features yield typical runtimes of only 1s. Applications include semantic visual navigation, image tagging for retrieval, and initializing organ-specific processing.

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Section: Introductionmentioning

confidence: 99%

Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

Criminisi

Shotton

Robertson

et al. 2011

Lecture Notes in Computer Science

202

159

View full text Add to dashboard Cite

show abstract

“…[53][54][55][56] Campadelli et al highlighted respective advantages and drawbacks that limit the use of such techniques in the clinic.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

et al. 2010

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Purpose:To investigate the potential of the normalized probabilistic atlases and computer-aided medical image analysis to automatically segment and quantify livers and spleens for extracting imaging biomarkers ͑volume and height͒. Methods: A clinical tool was developed to segment livers and spleen from 257 abdominal contrastenhanced CT studies. There were 51 normal livers, 44 normal spleens, 128 splenomegaly, 59 hepatomegaly, and 23 partial hepatectomy cases. 20 more contrast-enhanced CT scans from a public site with manual segmentations of mainly pathological livers were used to test the method. Data were acquired on a variety of scanners from different manufacturers and at varying resolution. Probabilistic atlases of livers and spleens were created using manually segmented data from ten noncontrast CT scans ͑five male and five female͒. The organ locations were modeled in the physical space and normalized to the position of an anatomical landmark, the xiphoid. The construction and exploitation of liver and spleen atlases enabled the automated quantifications of liver/spleen volumes and heights ͑midhepatic liver height and cephalocaudal spleen height͒ from abdominal CT data. The quantification was improved incrementally by a geodesic active contour, patient specific contrast-enhancement characteristics passed to an adaptive convolution, and correction for shape and location errors. Results: The livers and spleens were robustly segmented from normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 96.2%/92.7%, the volume/height errors were 2.2%/2.8%, the root-mean-squared error ͑RMSE͒ was 2.3 mm, and the average surface distance ͑ASD͒ was 1.2 mm. The spleen quantification led to 95.2%/91% Dice/Tanimoto overlaps, 3.3%/ 1.7% volume/height errors, 1.1 mm RMSE, and 0.7 ASD. The correlations ͑R 2 ͒ with clinical/ manual height measurements were 0.97 and 0.93 for the spleen and liver, respectively ͑p Ͻ 0.0001͒. No significant difference ͑p Ͼ 0.2͒ was found comparing interobserver and automaticmanual volume/height errors for liver and spleen. Conclusions: The algorithm is robust to segmenting normal and enlarged spleens and livers, and in the presence of tumors and large morphological changes due to partial hepatectomy. Imaging biomarkers of the liver and spleen from automated computer-assisted tools have the potential to assist the diagnosis of abdominal disorders from routine analysis of clinical data and guide clinical management.

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“…Other works are based on the construction of probabilistic atlases. That is the case of the works presented in (Park et al, 2003;Zhou et al, 2005;Shimizu et al, 2006). First, a registration step of training CT images into a standard space defined by landmarks (manually or automatically chosen) is needed.…”

Section: State Of the Artmentioning

confidence: 99%

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Fernandez-de-Manuel¹,

Ledesma-Carbayo²,

Jiménez-Carretero³

et al. 2011

Theory and Applications of CT Imaging and Analysis

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Simultaneous segmentation of multi-organ in three dimensional abdominal CT images

Cited by 17 publications

References 0 publications

Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

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