A Grading Strategy for Nuclear Pleomorphism in Histopathological Breast Cancer Images Using a Bag of Features (BOF)

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• 关键词：Breast cancer ; Histopathology ; Biomedical ; Nuclear pleomorphism
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9423
• 期：1
• 页码：75-82
• 全文大小：1,999 KB
• 参考文献：1.Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37 (1960)CrossRef
  2.Cosatto, E., Miller, M., Graf, H., Meyer, J.: Grading nuclear pleomorphism on histological micrographs, pp. 1–4, December 2008
  3.Dalle, J.R., Li, H., Huang, C.H., Leow, W.K., Racoceanu, D., Putti, T.C.: Nuclear pleomorphism scoring by selective cell nuclei detection. In: IEEE Workshop on Applications of Computer Vision Snowbird, Utah, USA (2009)
  4.Elston, C.W., Ellis, I.O.: Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 1991; 19; 403–410. Histopathology 41(3a), 151 (2002)
  5.Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D., Forman, D., Bray, F.: GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 (2012). http://​globocan.​iarc.​fr
  6.Fleiss, J.: Measuring nominal scale agreement among many raters. Psychological Bulletin 76(5), 378–382 (1971)CrossRef
  7.Galea, M., Blamey, R., Elston, C., Ellis, I.: The nottingham prognostic index in primary breast cancer. Breast Cancer Research and Treatment 22(3), 207–219 (1992). http://​dx.​doi.​org/​10.​1007/​BF01840834 CrossRef
  8.Green, A.R., Soria, D., Stephen, J., Powe, D.G., Nolan, C.C., Kunkler, I., Thomas, J., Kerr, G., Jack, W., Camreron, D., Piper, T., Ball, G.R., Garibaldi, J.M., Rakha, E.A., Bartlett, J.M., Ellis, I.O.: Nottingham prognostic index plus (npi+): Validation of the modern clinical decision making tool in breast cancer. Cancer Research 75(9), P5–09 (2015)
  9.Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28(1), 100–108 (1979). http://​www.​jstor.​org/​stable/​2346830 MATH
  10.Macenko, M., Niethammer, M., Marron, J., Borland, D., Woosley, J., Guan, X., Schmitt, C., Thomas, N.: A method for normalizing histology slides for quantitative analysis. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. ISBI 2009, pp. 1107–1110, June 2009
  11.Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference, pp. 36.1–36.10 (2002) doi:10.​5244/​C.​16.​36
  12.Meyer, J.S., Alvarez, C., Milikowski, C., Olson, N., Russo, I., Russo, J., Glass, A., Zehnbauer, B.A., Lister, K., Parwaresch, R.: Breast carcinoma malignancy grading by Bloom-Richardson system vs proliferation index reproducibility of grade and advantages of proliferation index. Modern pathology: an official journal of the United States and Canadian Academy of Pathology, Inc 18(8), 1067–1078 (2005)CrossRef
  13.National Cancer Institute: The Cancer Genoma Atlas. http://​cancergenome.​nih.​gov/​
  14.Prvulović, I., Kardum-Skelin, I., Sustercić, D., Jakić-Razumović, J., Manojlović, S.: Morphometry of tumor cells in different grades and types of breast cancer. Collegium antropologicum 34(1), 99–103 (2010)
  15.Romo, D., Garcez, P., Romero, E.: A discriminant multi-scale histopathology descriptor using dictionary learning. In: Proc. SPIE 9041, 90410Q–90410Q-6 (2014)
  16.Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 480–492 (2012)CrossRef
  17.Veta, M., Kornegoor, R., Huisman, A., Verschuur-Maes, A.H.J., Viergever, M.A., Pluim, J.P.W., van Diest, P.J.: Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 25(12), 1559–15565 (2012)CrossRef
  18.Veta, M., Van Diest, P.J., Kornegoor, R., Huisman, A., Viergever, M.A., Pluim, J.P.W.: Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images. PLoS ONE 8(7), e70221 (2013)CrossRef
  
• 作者单位：Ricardo Moncayo (15)
  David Romo-Bucheli (15)
  Eduardo Romero (15)
  
  15. CIM@LAB, Universidad Nacional de Colombia, Carrera 45 No 26-85, Bogotá, Colombia
  
• 丛书名：Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
• ISBN：978-3-319-25751-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Nuclear pleomorphism is an early breast cancer (BCa) indicator that assesses any nuclear size, shape or chromatin appearance variations. Research involving the ranking by several experts shows that kappa coefficient ranges from 0.3(low) to 0.5 (moderate)[12]. In this work, an automatic grading approach for nuclear pleomorphism is proposed. First, a large nuclei sample is characterized by a multi-scale descriptor that is then assigned to the most similar atom of a previously learned dictionary. An occurrence histogram represents then any Field of View (FoV) in terms of the occurrence of the descriptors with respect to the learned atoms of the dictionary. Finally, a SVM classifier assigns a full pleomorphism grading, between 1 and 3, using the previous histogram. The strategy was evaluated extracting 134 FoV (\(\times 20\)), graded by a pathologist, from 14 BCa slides of ’The Cancer Genome Atlas’ (TCGA) database.The obtained precision and recall measures were 0.67 and 0.67.