Bayesian Landmark-Based Shape Analysis of Tumor Pathology Images

References

• Charpiat, G., Faugeras, O., and Keriven, R. (2003), “Shape Metrics, Warping and Statistics,” in Proceedings 2003 International Conference on Image Processing (Vol. 2), IEEE, pp. II–627.
   Google Scholar
• Cox, D. R. (1972), “Regression Models and Life-Tables,” Journal of the Royal Statistical Society, Series B, 34, 187–202. DOI: 10.1111/j.2517-6161.1972.tb00899.x.
   Web of Science ®Google Scholar
• Dagogo-Jack, I., and Shaw, A. T. (2018), “Tumour Heterogeneity and Resistance to Cancer Therapies,” Nature Reviews Clinical Oncology, 15, 81–94. DOI: 10.1038/nrclinonc.2017.166.
   PubMed Web of Science ®Google Scholar
• Dahl, D. B. (2006), “Model-based Clustering for Expression Data via a Dirichlet Process Mixture Model,” Bayesian Inference for Gene Expression and Proteomics, 4, 201–218.
   Google Scholar
• Domijan, K., and Wilson, S. (2005), “A Bayesian Method for Automatic Landmark Detection in Segmented Images,” in Learning Techniques for Processing Multimedia Content, p. 69.
   Google Scholar
• Fernández, E., Yang, S., Chiou, S. H., Moon, C., Zhang, C., Yao, B., Xiao, G., and Li, Q. (2022), “SAFARI: Shape Analysis for AI-Segmented Images,” BMC Medical Imaging, 22, 1–7. DOI: 10.1186/s12880-022-00849-8.
   PubMed Web of Science ®Google Scholar
• George, E. I., and McCulloch, R. E. (1997), “Approaches for Bayesian Variable Selection,” Statistica Sinica, 7, 339–373.
   Web of Science ®Google Scholar
• Haralick, R. M., Shanmugam, K., and Dinstein, I. H. (1973), “Textural Features for Image Classification,” IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621. DOI: 10.1109/TSMC.1973.4309314.
   Web of Science ®Google Scholar
• Hubert, L., and Arabie, P. (1985), “Comparing Partitions,” Journal of Classification, 2, 193–218. DOI: 10.1007/BF01908075.
   Web of Science ®Google Scholar
• Jiang, S., Zhou, Q., Zhan, X., and Li, Q. (2021), “BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies,” Journal of Data Science, 19, 365–389. DOI: 10.6339/21-JDS1009.
   Google Scholar
• Kijima, S., Sasaki, T., Nagata, K., Utano, K., Lefor, A. T., and Sugimoto, H. (2014), “Preoperative Evaluation of Colorectal Cancer Using CT Colonography, MRI, and PET/CT,” World Journal of Gastroenterology, 20, 16964. DOI: 10.3748/wjg.v20.i45.16964.
   PubMed Web of Science ®Google Scholar
• Kilday, J., Palmieri, F., and Fox, M. D. (1993), “Classifying Mammographic Lesions Using Computerized Image Analysis,” IEEE Transactions on Medical Imaging, 12, 664–669. DOI: 10.1109/42.251116.
   PubMed Web of Science ®Google Scholar
• Kneip, A., and Ramsay, J. O. (2008), “Combining Registration and Fitting for Functional Models,” Journal of the American Statistical Association, 103, 1155–1165. DOI: 10.1198/016214508000000517.
   Web of Science ®Google Scholar
• Larroza, A., Bodí, V., and Moratal, D. (2016), “Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications,” in Assessment of Cellular and Organ Function and Dysfunction using Direct and Derived MRI Methodologies, ed. C. Constantinides, pp. 75–106, London: IntechOpen.
   Google Scholar
• Liu, Y., Sajja, B. R., Uberti, M. G., Gendelman, H. E., Kielian, T., and Boska, M. D. (2012), “Landmark Optimization Using Local Curvature for Point-based Nonlinear Rodent Brain Image Registration,” International Journal of Biomedical Imaging, 2012, 635207. DOI: 10.1155/2012/635207.
   PubMedGoogle Scholar
• Luo, X., Zang, X., Yang, L., Huang, J., Liang, F., Canales, J. R., Wistuba, I. I., Gazdar, A., Xie, Y., and Xiao, G. (2016), “Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis,” Journal of Thoracic Oncology. 12, 501–509. DOI: 10.1016/j.jtho.2016.10.017.
   PubMed Web of Science ®Google Scholar
• Matthews, B. W. (1975), “Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme,” Biochimica et Biophysica Acta (BBA)-Protein Structure, 405, 442–451. DOI: 10.1016/0005-2795(75)90109-9.
   PubMed Web of Science ®Google Scholar
• Meacham, C. E., and Morrison, S. J. (2013), “Tumour Heterogeneity and Cancer Cell Plasticity,” Nature, 501, 328–337. DOI: 10.1038/nature12624.
   PubMed Web of Science ®Google Scholar
• Mohammadzadeh, Z., Safdari, R., Ghazisaeidi, M., Davoodi, S., and Azadmanjir, Z. (2015), “Advances in Optimal Detection of Cancer by Image Processing: Experience with Lung and Breast Cancers,” Asian Pacific Journal of Cancer Prevention, 16, 5613–5618. DOI: 10.7314/apjcp.2015.16.14.5613.
   PubMedGoogle Scholar
• Niazi, M. K. K., Parwani, A. V., and Gurcan, M. N. (2019), “Digital Pathology and Artificial Intelligence,” The Lancet Oncology 20, e253–e261. DOI: 10.1016/S1470-2045(19)30154-8.
   PubMed Web of Science ®Google Scholar
• Pascual-Vargas, P., Cooper, S., Sero, J., Bousgouni, V., Arias-Garcia, M., and Bakal, C. (2017), “RNAi Screens for Rho GTPase Regulators of Cell Shape and YAP/TAZ Localisation in Triple Negative Breast Cancer,” Scientific Data, 4, 1–13. DOI: 10.1038/sdata.2017.18.
   Web of Science ®Google Scholar
• Pebesma, E. (2018), “Simple Features for R: Standardized Support for Spatial Vector Data,” The R Journal 10, 439–446. DOI: 10.32614/RJ-2018-009.
   Google Scholar
• Rand, W. M. (1971), “Objective Criteria for the Evaluation of Clustering Methods,” Journal of the American Statistical Association, 66, 846–850. DOI: 10.1080/01621459.1971.10482356.
   Web of Science ®Google Scholar
• Sadimin, E. T., and Foran, D. J. (2012), “Pathology Imaging Informatics for Clinical Practice and Investigative and Translational Research,” North American Journal of Medicine and Science, 5, 103–109. DOI: 10.7156/v5i2p103.
   Google Scholar
• Santos, J. M., and Embrechts, M. (2009), “On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification,” in International Conference on Artificial Neural Networks, pp. 175–184, Springer.
   Google Scholar
• Shimrat, M. (1962), “Algorithm 112: Position of Point Relative to Polygon,” Communications of the ACM, 5, 434. DOI: 10.1145/368637.368653.
   Web of Science ®Google Scholar
• Strait, J., Chkrebtii, O., and Kurtek, S. (2019), “Automatic Detection and Uncertainty Quantification of Landmarks on Elastic Curves,” Journal of the American Statistical Association, 114, 1002–1017. DOI: 10.1080/01621459.2018.1527224.
   PubMed Web of Science ®Google Scholar
• Subburaj, K., Ravi, B., and Agarwal, M. (2008), “3D Shape Reasoning for Identifying Anatomical Landmarks,” Computer-Aided Design and Applications, 5, 153–160. DOI: 10.3722/cadaps.2008.153-160.
   Google Scholar
• Tabesh, A., Teverovskiy, M., Pang, H.-Y., Kumar, V. P., Verbel, D., Kotsianti, A., and Saidi, O. (2007), “Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images,” IEEE Transactions on Medical Imaging, 26, 1366–1378. DOI: 10.1109/TMI.2007.898536.
   PubMed Web of Science ®Google Scholar
• Tadesse, M. G., Sha, N., and Vannucci, M. (2005), “Bayesian Variable Selection in Clustering High-Dimensional Data,” Journal of the American Statistical Association, 100, 602–617. DOI: 10.1198/016214504000001565.
   Web of Science ®Google Scholar
• Wang, S., Chen, A., Yang, L., Cai, L., Xie, Y., Fujimoto, J., Gazdar, A., and Xiao, G. (2018), “Comprehensive Analysis of Lung Cancer Pathology Images to Discover Tumor Shape and Boundary Features that Predict Survival Outcome,” Scientific Reports, 8, 1–9. DOI: 10.1038/s41598-018-27707-4.
   PubMedGoogle Scholar
• Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., and Snyder, M. (2016), “Predicting Non-Small Cell Lung Cancer Prognosis by Fully Automated Microscopic Pathology Image Features,” Nature Communications, 7, 12474. DOI: 10.1038/ncomms12474.
   PubMed Web of Science ®Google Scholar
• Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Gräf, S., Chin, S.-F., Schwarz, R. F., Curtis, C., Dunning, M. J., Bardwell, H. et al. (2012), “Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling,” Science Translational Medicine, 4, 157ra143. DOI: 10.1126/scitranslmed.3004330.
   PubMed Web of Science ®Google Scholar
• Zulqarnain Gilani, S., Shafait, F., and Mian, A. (2015), “Shape-based Automatic Detection of a Large Number of 3D Facial Landmarks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4639–4648.
   Google Scholar