113
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches

&
Pages 30-45 | Received 29 Jul 2021, Accepted 21 Dec 2022, Published online: 21 Jan 2023

References

  • Siegel RL, Miller KD, Fuchs BS HE, et al. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33.
  • National cancer Institute United State. “Cancer Stat Facts: Non- Hodgkin Lymphoma”.https://seer.cancer.gov/statfacts/html/nhl.html (2020).
  • Teras LR, DeSantis CE, Cerhan JR, et al. 2016US lymphoid malignancy statistics by world health organization subtypes. CA: Cancer. 2016;66(6):443–459.
  • Hitz F, Kettererb N, Lohric A, et al. “Diagnosis and treatment of follicular lymphoma” European journal of medical sciences Swiss Med Weekly, August 2011.
  • Nair R, Arora N, Mallath MK. Epidemiology of Non-Hodgkin's lymphoma in India. Oncology. 2016;91:18–25.
  • Swerdlow S, Campo E, Harris N, et al. “WHO classification of tumors of haematopoietic and lymphoid tissues,” vol. 2, World Health Organization, Lyon, France, fourth ed. 2008.
  • Mabadhushi A, Xu J. Digital pathology image analysis: opportunities and challenges. Image Med. 2009;1:7–10.
  • Gurcan MN, Boucheron L, Can A, et al. Histopathology image analysis: a review. IEEE Rev Biomed Eng. 2009;2:147–171.
  • Madabhushi A, Ali S. An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Trans Med Imaging. July 2012;31(7):1448–1460.
  • Gogia A, Raina V, Kumar L, et al. Follicular lymphoma: an institutional analysis. Asian Pac J Cancer Prev. 2018;18:681–685.
  • Anneke G, Bouwer B, Imhoff GW, et al. Follicular lymphoma grade 3B includes 3 cytogenetically defined subgroups with primary t(14;18), 3q27, or other translations: t(14;18) and 3q27 are mutually exclusive. Blood J Hematol Libr. 2013;101(3):1149–1154.
  • Vahadane A, Peng T, Sethi A, et al. ‘Structure preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging. 2016;35(8):1962–1971.
  • Sertel O, Lozanski G, Shana'ah A, et al. Computer-aided detection of centroblast for follicular lymphoma grading using adaptive likelihood based cell segmentation. IEEE Trans Biomed Eng. 2010;57(10):2613–2616.
  • Wahlin BE, Birgitta Sander MD, Christensson B, et al. Grading follicular lymphoma: No difference between 1, 2 and 3a, but 3b is something else. Blood J. 2007;110(11):2611.
  • Oztan B, Kong H, Gurcan MN, et al. Follicular lymphoma grading using cell-graphs and multi-scale feature analysis. Med Imaging Proc SPIE. 2012;8315.
  • Belkacem-Boussaid K, Samsi S, Lozanski G, et al. Automatic detection of follicular regions in H&E images using iterative shape index. Comput Med Imaging Graph. 2011;35:592–602.
  • Zheng X, Lei Q, Yao R, et al. Image segmentation based on adaptive K-means algorithm. Eurasip J Image Video Proc. 2018;68. doi:10.1186/s13640-018-0309-3.
  • Öztürka Ş, Akdemirb B. Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA. International Conference on Computational Intelligence and Data Science, elseveir pp.40–46, 2018.
  • Jabid T, Kabir MH, Chae O. Local Directional Pattern (LDP) – a robust image descriptor for object recognition. 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance, 2010.
  • Shabat AM, Tapamo J. A comparative study of the use of local directional pattern for texture-based informal settlement classification. J Appl Res Technol. 2017;15:250–258.
  • Haryanto T, Pratama A, Suhartanto H, et al. Multipatch-GLCM for texture feature extraction on classification of the colon histopathology image using deep neural network with GPU acceleration. J Comput Sci. 2020;16(3):280–294.
  • Bhattacharjee S, Park H-G, Kim C-H, et al. Quantitative analysis of benign and malignant tumors in histopathology: predicting prostate cancer grading using SVM. Appl Sci MPDI J. 2019;9:2969.
  • Kanti Das B, Sekhar Dutta H. Infection level identification for leukemia detection using optimized support vector neural network. Imaging Sci J. 2019;67(8):417–433.
  • Syrykh C, Abreu A, Amara N, et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. Npj Digit Med, Nat. 2020;3:63.
  • Thiam Y, Vailhen N, Abreu A, et al. Artificial intelligence against lymphoma: a New deep learning based anatomopathology assistant to distinguish follicular lymphoma from follicular hyperplasia. European Hematol Ass Libr. Laurent C. 2020;298107:PB2193.
  • Brousset P, Syrykh C, Abreu A, et al. Diagnosis And classification assistance from lymphoma microscopic images using deep learning. Hematol Oncol by John Wiley & Sons. 2019;37(S2):18–22.
  • Rajathi V, Bhavani RR, Wiselin Jiji G. Varicose ulcer(C6) wound image tissue classification using multidimensional convolutional neural networks. Imaging Sci J. 2019;67(7):374–384.
  • American Cancer Society http://www.cancer.org.
  • Kong J, Sertel O, Gewirtz A, et al. Development of computer-based system to aid pathologists in histological grading of follicular lymphomas. GA Am Soc Histol. 2007: 3318.
  • Li C, Huang R, Ding Z, et al. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process. 2011;20(7):2007–2016.
  • Sertel O, Kong J, Lozanski G, et al. Computerized microscopic image analysis of follicular lymphoma. SPIE. 2008;6915: 691535.
  • Sertel O, Kong J, Catalyurek U, et al. Histopathological image analysis using model-based intermediate representations and color texture: follicular lymphoma grading. J Signal Proc Sys. 2009;55:169–183.
  • Sertel O, Kong J, Catalyurek U, et al. Texture classification using non-linear colour quantization: application to histopathological image analysis. IEEE ICASSP'08; Las Vegas, NV (2008).
  • Samsi S, Lozanski G, Shana'ah A, et al. Detection of follicles from IHC stained slide of follicular lymphoma using iterative watershed. IEEE Trans Biomed Eng. 2010;57(10):2609–2612.
  • Belkacem-Boussaid K, Pennell M, Lozanski G, et al. Computer-aided classification of centroblast cells in follicular lymphoma. Anal Quant Cytol Histol. 2010;32(5):254–260.
  • Kong H, Gurcan MN, Belkacem-Boussaid K. Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging. 2011;30(9):1661–1677.
  • Samsi S, Krishnamurthy AK, Gurcan MN. An efficient computational framework for the analysis of whole slide images: application to follicular lymphoma immunohistochemistry. J Comput Sci. 2012;3:269–279.
  • Dimitropoulos K, Barmpoutis P, Koletsa T, et al. Automated detection and classification of nuclei in pax5 and H&E-stained tissue sections of follicular lymphoma. Signal Image Vide Process. 2016;11(1):145–153.
  • Michail E, Kornaropoulos EN, Dimitropoulos K, et al. Detection of CentroblastsIn H&E-stained images of follicular lymphoma. 2014 IEEE 22nd Signal Processing and Communications Applications Conference 2319–2322, 2014.
  • Kornaropoulos EN, Khan Niazi MK, Lozanski G, et al. Histopathological image analysis for centroblasts classification through dimensionality reduction approaches. Cytom Anal. 2014;85(3):242–255.
  • Fauzi MFA, Pennell M, Sahiner B, et al. Classification of follicular lymphoma: the effect of computer aid on pathologists grading. BMC Med Inform Decis Mak. 2015;15.26715518.
  • Dimitropoulos K, Michail E, Koletsa T, et al. Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma. Signal Image Vide Process. 2014;8(1):33–40.
  • Abas FS, Shana'ah A, Christian B, et al. Computer-assisted quantification of CD3+ T cells in follicular lymphoma. Int Soc Advancement Cytom. 2017;91(6):609–621.
  • Bayramoglu N, Heikkil J. Transfer learning for cell nuclei classification in histopathology images. Eur Conf Comput Vis. 2016;9915:532–539.
  • Sertel O, Catalyurek U, Lozanski G, et al. An image analysis approach for detecting malignant cells in digitized H&E-stained histology images of follicular lymphoma. International Conference on Pattern Recognition, 2010.
  • Oger M, Belhomme P, Gurcan MN. A general framework for the segmentation of follicular lymphoma virtual slides. Comput Med Imaging Graph. 2012;36:442–451.
  • Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn. 2012;12:281–305.
  • Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Int Jt Conf Artif Intell. 1995;14(2):1137–1145.
  • Thaína A, Azevedo T, Leandro A. Segmentation methods of HandE-stained histological images of lymphoma: a review. Informatics Med. 2017;9:35–43.
  • Xing F, Yang L. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE Rev Biomed Eng. 2016: 234–263.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.