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. 2009 Jun;22(3):286-96.
doi: 10.1007/s10278-008-9134-z. Epub 2008 Aug 14.

Shape priors for segmentation of the cervix region within uterine cervix images

Shelly Lotenberg  1 , Shiri Gordon, Hayit Greenspan
Affiliations

Shape priors for segmentation of the cervix region within uterine cervix images

Shelly Lotenberg et al. J Digit Imaging. 2009 Jun.

Abstract

The work focuses on a unique medical repository of digital uterine cervix images ("cervigrams") collected by the National Cancer Institute (NCI), National Institute of Health, in longitudinal multiyear studies. NCI together with the National Library of Medicine is developing a unique web-based database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for the automated analysis of the cervigram content to support the cancer research. In recent works, a multistage automated system for segmenting and labeling regions of medical and anatomical interest within the cervigrams was developed. The current paper concentrates on incorporating prior-shape information in the cervix region segmentation task. In accordance with the fact that human experts mark the cervix region as circular or elliptical, two shape models (and corresponding methods) are suggested. The shape models are embedded within an active contour framework that relies on image features. Experiments indicate that incorporation of the prior shape information augments previous results.

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Figures

Fig 1
Fig 1
Cervix region examples marked by a medical expert with specialized experience in gynecological oncology.
Fig 2
Fig 2
Incorporating prior shape information in the curve evolution functional. a Manual marking of the expert (blue); b cervix boundary results: initial ROI (green); boundary detected using data and shape terms in a single-stage procedure (red); boundary detected using the two-stage procedure (white); c curvature feature map (bright regions correspond to local concavities).
Fig 3
Fig 3
Effect of local weights on boundary detection quality. Left image—manual markings of the expert (blue). Right image—boundary detection results: Datadriven curve (green); Equally weighted shape term (red); Locally weighted shape term (white).
Fig 4
Fig 4
Cervix boundary detection. Datadriven contour, based on color and curvature features—marked in green; final contour, following refinement with a circular prior—marked in red; expert markings imposed in blue. Hausdorff (H), Dice (D), Sensitivity (S) and False Positives (FP), for the datadriven (data) and the circular shape prior (shape) contours are listed under corresponding cervigrams.
Fig 5
Fig 5
Cervix boundary segmentation with different shape priors. Manual markings imposed in blue; circular prior results imposed in red and elliptical prior results, imposed in white.
Fig 6
Fig 6
A comparison between circular (left box plot) and elliptical (right box plot) shape priors. Top row: Circular subset results. Bottom row: elliptical subset results. Presented are box plot results for: (I) Dice, (II) Sensitivity, (III) FP and (IV) Hausdorff Distance.
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References

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