Metastasis detection from whole slide images using local features and random forests

Cytometry Part A

Volumn 91, Issue 6, 2017, Pages 555-565

  Valkonen, Mira ^a   Kartasalo, Kimmo ^a   Liimatainen, Kaisa ^a   Nykter, Matti ^a   Latonen, Leena ^a   Ruusuvuori, Pekka ^a  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

breast cancer; computer aided diagnosis; digital pathology; machine learning; metastasis detection; random forest; sentinel lymph nodes; whole slide images

Indexed keywords

EOSIN; HEMATOXYLIN;

ADULT; AREA UNDER THE CURVE; BREAST TUMOR; CELL NUCLEUS; COMPUTER ASSISTED DIAGNOSIS; CYTOCHEMISTRY; DIAGNOSTIC IMAGING; FEMALE; HUMAN; LYMPH NODE; LYMPH NODE METASTASIS; LYMPHOCYTE; MACHINE LEARNING; MIDDLE AGED; PATHOLOGY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; SOFTWARE; STATISTICS AND NUMERICAL DATA; ULTRASTRUCTURE;

ADULT; AREA UNDER CURVE; BREAST NEOPLASMS; CELL NUCLEUS; EOSINE YELLOWISH-(YS); FEMALE; HEMATOXYLIN; HISTOCYTOCHEMISTRY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LYMPH NODES; LYMPHATIC METASTASIS; LYMPHOCYTES; MACHINE LEARNING; MIDDLE AGED; ROC CURVE; SOFTWARE;

EID: 85018639694     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.23089     Document Type: Article

References (41)

1. Pantanowitz L, Valenstein PN, Evans AJ, Kaplan KJ, Pfeifer JD, Wilbur DC, Collins LC, Colgan TJ. Review of the current state of whole slide imaging in pathology. J Pathol Inform 2011;2:36.
2. Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: A review. IEEE Rev Biomed Eng 2009;2:147–171.
3. Ghaznavi F, Evans A, Madabhushi A, Feldman M. Digital imaging in pathology: Whole-slide imaging and beyond. Annu Rev Pathol 2013;8:331–359.
4. Bejnordi BE, Litjens G, Hermsen M, Karssemeijer N, van der Laak JA. A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images. In: Proceedings of the International Society for Optics and Photonics (SPIE) Conference on Medical Imaging, Orlando, FL, USA; 2015. p 94200H-94200H.
5. Khan AM, Rajpoot N, Treanor D, Magee D. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng 2014;61:1729–1738.
6. Veta M, van Diest PJ, Kornegoor R, Huisman A, Viergever MA, Pluim JP. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PLoS One 2013;8:e70221.
7. Kothari S, Phan JH, Stokes TH, Wang MD. Pathology imaging informatics for quantitative analysis of whole-slide images. J Am Med Inform Assoc 2013;20:1099–1108.
8. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. Lyon, France: International Agency for Research on Cancer; 2013. Available from: http://globocan.iarc.fr, accessed on 27/4/2016.
9. Ran S, Volk L, Hall K, Flister MJ. Lymphangiogenesis and lymphatic metastasis in breast cancer. Pathophysiology 2010;17:229–251.
10. Veta M, Pluim JP, van Diest PJ, Viergever MA. Breast cancer histopathology image analysis: A review. IEEE Trans Biomed Eng 2014;61:1400–1411.
11. Niwas SI, Palanisamy P, Sujathan K, Bengtsson E. Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex Daubechies wavelets. Signal Process 2013;93:2828–2837.
12. Abas FS, Gokozan HN, Goksel B, Otero JJ, Gurcan MN. Intraoperative neuropathology of glioma recurrence: Cell detection and classification. In: Proceedings of the International Society for Optics and Photonics (SPIE) Conference on Medical Imaging, San Diego, CA, USA; 2016. p 979109-979109.
13. Kornaropoulos EN, Niazi M, Lozanski G, Gurcan MN. Histopathological image analysis for centroblasts classification through dimensionality reduction approaches. Cytometry Part A 2014;85A:242–255.
14. Doyle S, Feldman M, Tomaszewski J, Madabhushi A. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans Biomed Eng 2012;59:1205–1218.
15. Turkki R, Linder N, Kovanen PE, Pellinen T, Lundin J. Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: Texture-based classification of tissue morphologies. In: Proceedings of the International Society for Optics and Photonics (SPIE) Conference on Medical Imaging, San Diego, CA, USA; 2016. p 979110-979110.
16. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33:1–22.
17. Breiman L. Random forests. Mach Learn 2001;45:5–32. 1
18. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA; 2012. pp 1097–1105.
19. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–444.
20. Sirinukunwattana K, Raza SEA, Tsang YW, Snead D, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2016;35:1196–1206.
21. Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, Tomaszewski J, Gonzalez F, Madabhushi A. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging 2014;1:034003.
22. Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013. Berlin, Heidelberg: Springer; 2013. pp 411–418.
23. Chen H, Qi X, Yu L, Heng PA. DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation. arXiv preprint arXiv 2016; 1604.02677.
24. Sirinukunwattana K, Pluim JPW, Chen H, Qi X, Heng P-A, Guo YB, Wang LY, Matuszewski BJ, Bruni E, Sanchez U, et al. Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest. arXiv preprint arXiv 2016; 1603.00275.
25. Ruusuvuori P, Valkonen M, Nykter M, Visakorpi T, Latonen L. Feature-based analysis of mouse prostatic intraepithelial neoplasia in histological tissue sections. J Pathol Inform 2016;7:5.
26. Otsu N. A threshold selection method from gray-level histograms. Automatica 1975;11:23–27.
27. Gonzalez RC, Woods RE. Digital Image Processing, 2nd ed. New Jersey, Upper Saddle River: Prentice hall, Inc.; 2002.
28. Tuominen VJ, Isola J. Linking whole-slide microscope images with DICOM by using JPEG2000 interactive protocol. J Digit Imaging 2010;23:454–462.
29. Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 2001;23:291–299.
30. Bradley D, Roth G. Adaptive thresholding using the integral image. J Graph Gpu Game Tools 2007;12:13–21.
31. Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002;24:971–987.
32. Pietikäinen M, Ojala T, Xu Z. Rotation-invariant texture classification using feature distributions. Pattern Recognit 2000;33:43–52.
33. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004;60:91–110.
34. Ludwig O, Delgado D, Goncalves V, Nunes U. Trainable Classifier-fusion Schemes: An Application to Pedestrian Detection. In: Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA; 2009. pp 432–437.
35. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 1st IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA; 2005. pp 886–893.
36. Matas J, Chum O, Urban M, Pajdla T. Robust wide-baseline stereo from maximally stable extremal regions. Image Vis Comput 2004;22:761–767.
37. Vedaldi A, Fulkerson B. VLFeat: An Open and Portable Library of Computer Vision Algorithms. In: Proceedings of the 18th ACM International Conference on Multimedia, Firenze, Italy; 2010. pp 1469–1472.
38. Sertel O, Kong J, Shimada H, Catalyurek UV, Saltz JH, Gurcan MN. Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development. Patt Recogn 2009;42:1093–1103.
39. Yu KH, Zhang C, Berry GJ, Altman RB, Ré C, Rubin DL, Snyder M. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 2016;7:12474.
40. Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. arXiv preprint 2016; arXiv:1606.05718.
41. Chen R, Jing Y, Jackson H. identifying metastases in sentinel lymph nodes with deep convolutional neural networks. arXiv preprint 2016; arXiv:1608.01658.