Symmetric Curvature Patterns for Colonic Polyp Detection

Jerebko, Anna K.; Lakare, Sarang; Cathier, Pascal; Periaswamy, Senthil; Bogoni, Luca

doi:10.1007/11866763_21

Cited by 24 publications

(42 citation statements)

References 10 publications

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“…The observed sensitivity was comparable to the sensitivity of radiologists using CTC [7], [15], [16] and competed with other CAD systems [7]- [9], [26]. It was also shown that the CAD system generalizes well to data sets from different medical centers.…”

Section: Discussion/conclusionmentioning

confidence: 52%

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Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression

Ravesteijn

Wijk

Vos

et al. 2010

IEEE Trans. Med. Imaging

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Abstract-We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. The CAD system consists of two steps: candidate detection and supervised classification. The characteristics of the detection step lead to specific choices for the classification system. The candidates are ordered by a linear logistic classifier (logistic regression) based on only three features: the protrusion of the colon wall, the mean internal intensity, and a feature to discard detections on the rectal enema tube. This classifier can cope with a small number of polyps available for training, a large imbalance between polyps and non-polyp candidates, a truncated feature space, unbalanced and unknown misclassification costs, and an exponential distribution with respect to candidate size in feature space. Our CAD system was evaluated with data sets from four different medical centers. For polyps larger than or equal to 6 mm we achieved sensitivities of respectively 95%, 85%, 85%, and 100% with 5, 4, 5, and 6 false positives per scan over 86, 48, 141, and 32 patients. A cross-center evaluation in which the system is trained and tested with data from different sources showed that the trained CAD system generalizes to data from different medical centers and with different patient preparations. This is essential to application in large-scale screening for colorectal polyps.

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Section: Discussion/conclusionmentioning

confidence: 52%

“…Yoshida and Näppi used linear and quadratic discriminant classifiers [21], [22], [25] as well as Jerebko et al [26]. Wang et al [27] used a two-level classifier with a further unspecified linear discriminant classifier in the second level.…”

Section: A Related Workmentioning

confidence: 99%

Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression

Ravesteijn

Wijk

Vos

et al. 2010

IEEE Trans. Med. Imaging

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“…We propose a stratified learning framework including (supervised) objectspecific image segmentation, segmentation feature extraction, robust object classification and importance regression (taking into account the segmentation ambiguity and uncertainty). Segmentation has been extensively explored for various medical imaging purpose [27,15,29,8,22,11,24], but explicit descriptive feature extraction and analysis on statistically characterizing segmentation outputs, for object classification and robust staging of cancer (as estimate of object key attributes), has not been much studied. In this paper, we focus on finding and analyzing polyps 1 , the precursors of colon cancer, but the proposed method can be extended to other medical imaging applications (e.g., lung nodule detection), or generic object segmentation and detection tasks in 3D range, LIDAR or spatial-temporal volumetric data.…”

Section: Introductionmentioning

confidence: 99%

“…It can be heuristic, non-probabilistic [15,29,8,24], or data-driven learned and probabilistic [12]. In [12], a compositional polyp segmentation framework is proposed (locating possible polyp tips; finding inside/outside polyp surface voxels; and optimizing polyp boundaries), by supervisedly learning the medical experts' knowledge as the annotated polyp boundary curves in a database.…”

Section: Introductionmentioning

confidence: 99%

Effective 3D object detection and regression using probabilistic segmentation features in CT images

Lü¹,

Bi²,

Wolf³

et al. 2011

CVPR 2011

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Abstract3D object detection and importance regression/ranking are at the core for semantically interpreting 3D medical images of computer aided diagnosis (CAD). In this paper, we propose effective image segmentation features and a novel multiple instance regression method for solving the above challenges. We perform supervised learning based segmentation algorithm on numerous lesion candidates (as 3D VOIs: Volumes Of Interest in CT images) which can be true or false. By assessing the statistical properties in the joint space of segmentation output (e.g., a 3D class-specific probability map or cloud), and original image appearance, 57 descriptive features in six subgroups are derived. The new feature set shows excellent performance on effectively classifying ambiguous positive and negative VOIs, for our CAD system of detecting colonic polyps using CT images. The proposed regression model on our segmentation derived features behaves as a robust object (polyp) size/importance estimator and ranking module with high reliability, which is critical for automatic clinical reporting and cancer staging. Extensive evaluation is executed on a large clinical dataset of 770 CT scans from 12 medical sites for validation, with the best state-of-the-art results.

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“…More recently, modeling through spherical harmonics [10], surface normal overlap [17] and other curvature-based methods have been developed [1,26]. The use of small to moderate [5,8,26] and of large [25] feature sets followed by a more sophisticated classification mechanism have also been explored.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Model for Haustral Curvatures with Applications to Colon CAD

Melonakos

Mendonça

Bhotka

et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

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Abstract. Among the many features used for classification in computer-aided detection (CAD) systems targeting colonic polyps, those based on differences between the shapes of polyps and folds are most common. We introduce here an explicit parametric model for the haustra or colon wall. The proposed model captures the overall shape of the haustra and we use it to derive the probability distribution of features relevant to polyp detection. The usefulness of the model is demonstrated through its application to a colon CAD algorithm.

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Symmetric Curvature Patterns for Colonic Polyp Detection

Cited by 24 publications

References 10 publications

Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression

Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression

Effective 3D object detection and regression using probabilistic segmentation features in CT images

A Probabilistic Model for Haustral Curvatures with Applications to Colon CAD

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