Abstract
We present a high-throughput computer-aided system for the segmentation and classification of glands in high resolution digitized images of needle core biopsy samples of the prostate. It will allow for rapid and accurate identification of suspicious regions on these samples. The system includes the following three modules: 1) a hierarchical frequency weighted mean shift normalized cut (HNCut) for initial detection of glands; 2) a geodesic active contour (GAC) model for gland segmentation; and 3) a diffeomorphic based similarity (DBS) feature extraction for classification of glands as benign or cancerous. HNCut is a minimally supervised color based detection scheme that combines the frequency weighted mean shift and normalized cuts algorithms to detect the lumen region of candidate glands. A GAC model, initialized using the results of HNCut, uses a color gradient based edge detection function for accurate gland segmentation. Lastly, DBS features are a set of morphometric features derived from the nonlinear dimensionality reduction of a dissimilarity metric between shape models. The system integrates these modules to enable the rapid detection, segmentation, and classification of glands on prostate biopsy images. Across 23 H & E stained prostate studies of whole-slides, 105 regions of interests (ROIs) were selected for the evaluation of segmentation and classification. The segmentation results were evaluated on 10 ROIs and compared to manual segmentation in terms of mean distance (2.6 ±0.2 pixels), overlap (62±0.07%), sensitivity (85±0.01%), specificity (94±0.003%) and positive predictive value (68±0.08%). Over 105 ROIs, the classification accuracy for glands automatically segmented was (82.5 ±9.10%) while the accuracy for glands manually segmented was (82.89 ±3.97%); no statistically significant differences were identified between the classification results.
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References
Madabhushi, A.: Digital pathology image analysis: opportunities and challenges. Imaging in Medicine 1(1), 7–10 (2009)
Gleason, D.F.: Histologic grading of prostate cancer: A perspective. Human Pathology 23(3), 273–279 (1992); The Pathobiology of Prostate Cancer-Part 1
Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: A boosted bayesian multi-resolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Transactions on Biomedical Engineering (in Press)
Monaco, J.P., Tomaszewski, J.E., Feldman, M.D., Hagemann, I., Moradi, M., Mousavi, P., Boag, A., Davidson, C., Abolmaesumi, P., Madabhushi, A.: High-throughput detection of prostate cancer in histological sections using probabilistic pairwise markov models. Medical Image Analysis 14, 617–629 (2010)
Farjam, R., Soltanian-Zadeh, H., Jafari-Khouzani, K., Zoroofi, R.: An image analysis approach for automatic malignancy determination of prostate pathological images. Cytometry Part B (Clinical Cytometry) 72(B), 227–240 (2007)
Tabesh, A., Teverovskiy, M., Ho-Yuen, P., Kumar, V.P., Verbel, D., Kotsianti, A., Saidi, O.: Multifeature prostate cancer diagnosis and gleason grading of histological images. IEEE Transactions on Medical Imaging 26(10), 1366–1378 (2007)
Sparks, R., Madabhushi, A.: Novel morphometric based classification via diffeomorphic based shape representation using manifold learning. In: MICCAI 2010 (2010) (in press)
Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. International Journal of Computer Vision 46(3), 223–247 (2002)
Janowczyk, A., Chandran, S., Singh, R., Sasaroli, D., Coukos, G., Feldman, M.D., Madabhushi, A.: Hierarchical normalized cuts: Unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 230–238. Springer, Heidelberg (2009)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Agner, S., Soman, S., Libfeld, E., McDonald, M., Thomas, K., Englander, S., Rosen, M., Chin, D., Nosher, J., Madabhushi, A.: Textural kinetics: A novel dynamic contrast enhanced (DCE)- MRI feature for breast lesion classification. Journal of Digital Imaging (in press)
Cohen, L.D.: On active contour models and balloons. CVGIP: Image Underst. 53(2), 211–218 (1991)
Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: A new variational formulation. In: CVPR, vol. 1, pp. 430–436 (2005)
Sapiro, G.: Color snakes. Computer Vision and Image Understanding 68(2), 247–253 (1997)
Blum, H.: A transformation for extracting new descriptors of shape. In: Models for the Perception of Speech and Visual Form, pp. 367–380. MIT Press, Cambridge (1967)
Guo, H., Rangarajan, A., Joshi, S.: Diffeomorphic point matching. In: Handbook of Mathematical Models in Computer Vision, pp. 205–219. Springer, US (2005)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
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Xu, J., Sparks, R., Janowczyk, A., Tomaszewski, J.E., Feldman, M.D., Madabhushi, A. (2010). High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies. In: Madabhushi, A., Dowling, J., Yan, P., Fenster, A., Abolmaesumi, P., Hata, N. (eds) Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention. Prostate Cancer Imaging 2010. Lecture Notes in Computer Science, vol 6367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15989-3_10
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DOI: https://doi.org/10.1007/978-3-642-15989-3_10
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