TY - GEN
T1 - Adaptive energy selective active contour with shape priors for nuclear segmentation and gleason grading of prostate cancer
AU - Ali, Sahirzeeshan
AU - Veltri, Robert
AU - Epstein, Jonathan I.
AU - Christudass, Christhunesa
AU - Madabhushi, Anant
PY - 2011
Y1 - 2011
N2 - Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all 3 terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images. Morphological features extracted from these segmentations were found to able to discriminate different Gleason grade patterns with a classification accuracy of 84% via a Support Vector Machine classifier. On average the AdACM model provided 100% savings in computational times compared to a non-optimized hybrid AC model involving a shape prior.
AB - Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all 3 terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images. Morphological features extracted from these segmentations were found to able to discriminate different Gleason grade patterns with a classification accuracy of 84% via a Support Vector Machine classifier. On average the AdACM model provided 100% savings in computational times compared to a non-optimized hybrid AC model involving a shape prior.
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U2 - 10.1007/978-3-642-23623-5_83
DO - 10.1007/978-3-642-23623-5_83
M3 - Conference contribution
C2 - 22003675
AN - SCOPUS:82255185810
SN - 9783642236228
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 661
EP - 669
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 22 September 2011
ER -