Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images
Source abstract
Abstract
Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Head and Neck Cancer Studies · Oral Health Pathology and Treatment
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Napoli, Italy
Department of Mathematics, Computer Science, and Economics, University of Basilicata, 85100 Potenza, Italy
Allianz Benelux, 1000 Brussels, Belgium
Department of Computer Science, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
Funding
Regione Campania
References (43)
Show source-provided references
1. Oral Cavity Cancer10.1016/j.coms.2018.08.002 ↗
2. (2020, October 09). The Global Cancer Observatory. Available online: https://gco.iarc.fr/.
3. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries10.3322/caac.21492 ↗
4. Review of the current state of whole slide imaging in pathology10.4103/2153-3539.83746 ↗
5. Helin, H., Tolonen, T., Ylinen, O., Tolonen, P., Näpänkangas, J., and Isola, J. (2018). Optimized JPEG 2000 compression for efficient storage of histopathological whole-Slide images. J. Pathol. Inform., 9.10.4103/jpi.jpi_69_17 ↗
6. Whole slide imaging equivalency and efficiency study: Experience at a large academic center10.1038/s41379-019-0205-0 ↗
7. Digital pathology in clinical use: Where are we now and what is holding us back?10.1111/his.12993 ↗
8. Deep Learning for Whole Slide Image Analysis: An Overview10.3389/fmed.2019.00264 ↗
9. Xu, H., Park, S., and Hwang, T.H. (2019). Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images. IEEE/ACM Trans. Comput. Biol. Bioinform.10.1101/315648 ↗
10. Tian, K., Rubadue, C.A., Lin, D.I., Veta, M., Pyle, M.E., Irshad, H., and Heng, Y.J. (2019). Automated clear cell renal carcinoma grade classification with prognostic significance. PLoS ONE, 14.10.1101/661520 ↗
11. Jaber, M.I., Song, B., Taylor, C., Vaske, C.J., Benz, S.C., Rabizadeh, S., Soon-Shiong, P., and Szeto, C.W. (2020). A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival. Breast Cancer Res., 22.10.1186/s13058-020-1248-3 ↗
12. Tang, Z., Chuang, K.V., DeCarli, C., Jin, L.W., Beckett, L., Keiser, M.J., and Dugger, B.N. (2019). Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline. Nat. Commun., 10.10.1038/s41467-019-10212-1 ↗
13. Guo, Z., Liu, H., Ni, H., Wang, X., Su, M., Guo, W., Wang, K., Jiang, T., and Qian, Y. (2019). A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images. Sci. Rep., 9.10.1038/s41598-018-37492-9 ↗
14. Automatic Bone Marrow Cellularity Estimation in H&E Stained Whole Slide Images10.1002/cyto.a.23885 ↗
15. Bueno, G., Fernandez-Carrobles, M.M., Gonzalez-Lopez, L., and Deniz, O. (2020). Glomerulosclerosis identification in whole slide images using semantic segmentation. Comput. Methods Programs Biomed., 184.10.1016/j.cmpb.2019.105273 ↗
16. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review10.1016/j.oraloncology.2020.104885 ↗
17. Sun, Y.N., Wang, Y.Y., Chang, S.C., Wu, L.W., and Tsai, S.T. (2009). Color-based tumor tissue segmentation for the automated estimation of oral cancer parameters. Microsc. Res. Tech., 73.10.1002/jemt.20746 ↗
18. Textural pattern classification for oral squamous cell carcinoma10.1111/jmi.12611 ↗
19. Fouad, S., Randell, D., Galton, A., Mehanna, H., and Landini, G. (2017). Unsupervised morphological segmentation of tissue compartments in histopathological images. PLoS ONE, 12.10.1371/journal.pone.0188717 ↗
20. Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis10.1016/j.tice.2018.06.004 ↗
21. Baik, J., Ye, Q., Zhang, L., Poh, C., Rosin, M., MacAulay, C., and Guillaud, M. (2014). Automated classification of oral premalignant lesions using image cytometry and Random Forests-based algorithms. Cell. Oncol., 37.10.1007/s13402-014-0172-x ↗
22. Krishnan, M.M.R., Venkatraghavan, V., Acharya, U.R., Pal, M., Paul, R.R., Min, L.C., Ray, A.K., Chatterjee, J., and Chakraborty, C. (2012). Automated oral cancer identification using histopathological images: A hybrid feature extraction paradigm. Micron, 43.10.1016/j.micron.2011.09.016 ↗
23. Statistical analysis of textural features for improved classification of oral histopathological images10.1007/s10916-010-9550-8 ↗
24. Krishnan, M.M.R., Choudhary, A., Chakraborty, C., Ray, A.K., and Paul, R.R. (2011). Texture based segmentation of epithelial layer from oral histological images. Micron, 42.10.1016/j.micron.2011.03.003 ↗
25. Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis-An SVM based approach10.1016/j.compbiomed.2009.09.004 ↗
26. Brownian motion curve-based textural classification and its application in cancer diagnosis
27. Martino, F., Varricchio, S., Russo, D., Merolla, F., Ilardi, G., Mascolo, M., Dell’aversana, G.O., Califano, L., Toscano, G., and Pietro, G.D. (2020). A machine-learning approach for the assessment of the proliferative compartment of solid tumors on hematoxylin-eosin-stained sections. Cancers, 12.10.3390/cancers12051344 ↗
28. Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images10.1016/j.media.2019.101563 ↗
29. Micro-Net: A unified model for segmentation of various objects in microscopy images10.1016/j.media.2018.12.003 ↗
30. Rahman, T.Y., Mahanta, L.B., Das, A.K., and Sarma, J.D. (2020). Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips. Tissue Cell, 63.10.1016/j.tice.2019.101322 ↗
31. FABnet: Feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer10.1007/s00521-019-04516-y ↗
32. A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma10.1038/s41598-019-49710-z ↗
33. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: The CAMELYON dataset10.1093/gigascience/giy065 ↗
34. (2020, October 09). The Cancer Genome Atlas (TCGA), Available online: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.
35. The cancer genome atlas pan-cancer analysis project10.1038/ng.2764 ↗
36. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation10.1109/TPAMI.2016.2644615 ↗
37. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗
38. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA.
39. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
40. (2020, September 25). The National Cancer Institute (NCI), Available online: https://www.cancer.gov/.
41. Janocha, K., and Czarnecki, W.M. (2017). On Loss Functions for Deep Neural Networks in Classification. arXiv.10.4467/20838476SI.16.004.6185 ↗
42. Optimization Techniques to Improve Training Speed of Deep Neural Networks for Large Speech Tasks10.1109/TASL.2013.2284378 ↗
43. Fawakherji, M., Potena, C., Pretto, A., Bloisi, D.D., and Nardi, D. (2020). Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming. arXiv.10.1109/CCTA41146.2020.9206297 ↗