Nuclei Instance Segmentation of Cryosectioned H&E Stained Histological Images using Deep Learning

Ahmed, Zarif; Siddiqi, Chowdhury Nur e Alam; Alam, Fardifa Fathmiul

dc.contributor.author	Ahmed, Zarif
dc.contributor.author	Siddiqi, Chowdhury Nur e Alam
dc.contributor.author	Alam, Fardifa Fathmiul
dc.date.accessioned	2023-04-28T04:29:27Z
dc.date.available	2023-04-28T04:29:27Z
dc.date.issued	2022-04-30
dc.identifier.citation	[1] Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Pi˜neros M, et al. Global Cancer Observatory: Cancer Today. Lyon: International Agency for Research on Cancer; 2020 (https://gco.iarc.fr/today, accessed February 2021). [2] Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–674. doi: 10.1016/j.cell.2011.02.013. [PubMed] [CrossRef] [Google Scholar] [3] Titford, M. (2005). “The long history of hematoxylin”. Biotechnic Histochemistry. 80 (2): 73–80. doi:10.1080/10520290500138372. PMID 16195172. S2CID 20338201. [4] John K. C. Chan, “The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology,” International Journal of Surgical Pathology, vol. 22, no. 1, pp. 12–32, 2014. [5] Hariharan, B., Arbelez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization (2014), arXiv:1411.5752 [cs.CV] Prentice Hall Professional Technical Reference, 2002. [6] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE transactions on systems, man, and cybernetics, vol. 9, no. 1, pp. 62–66, 1979. [7] R. Nock and F. Nielsen, “Statistical region merging,” IEEE Transactions on pattern analysis and machine intelligence, vol. 26, no. 11, pp. 1452–1458, 2004. [8] N. Dhanachandra, K. Manglem, and Y. J. Chanu, “Image segmentation using k-means clustering algorithm and subtractive clustering algorithm,” Procedia Computer Science, vol. 54, pp. 764–771, 2015. [9] L. Najman and M. Schmitt, “Watershed of a continuous function,” Signal Processing, vol. 38, no. 1, pp. 99–112, 1994. 35 [10] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” International journal of computer vision, vol. 1, no. 4, pp. 321–331, 1988. [11] Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Transactions on pattern analysis and machine intelligence, vol. 23, no. 11, pp. 1222–1239, 2001. [12] N. Plath, M. Toussaint, and S. Nakajima, “Multi-class image segmentation using conditional random fields and global classification,” in Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009, pp. 817–824. [13] J.-L. Starck, M. Elad, and D. L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE transactions on image processing, vol. 14, no. 10, pp. 1570–1582, 2005. [14] S. Minaee and Y. Wang, “An admm approach to masked signal decomposition using subspace representation,” IEEE Transactions on Image Processing, vol. 28, no. 7, pp. 3192–3204, 2019. [15] L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587, 2017. [16] K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological cybernetics, vol. 36, no. 4, pp. 193–202, 1980 [17] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431– 3440. [18] O Ronneberger, P Fischer, T Brox, “U-net: Convolutional networks for biomedical image segmentation”, Medical Image Computing and ComputerAssisted Intervention (MICCAI), Springer, LNCS, Vol.9351: 234–241, 2015 36 [19] DA Novis and RJ Zarbo, “Interinstitutional comparison of frozen section turnaround time. a college of american pathologists q-probes study of 32868 frozen sections in 700 hospitals,” Archives of Pathology Amp; Laboratory Medicine, vol. 121, no. 6, pp. 559–567, June 1997. [20] Jevgenij Gamper, Navid Alemi Koohbanani, Simon Graham, Mostafa Jahanifar, Syed Ali Khurram, Ayesha Azam, Katherine Hewitt, and Nasir Rajpoot, “PanNuke dataset extension, insights and baselines,” arXiv preprint arXiv:2003.10778, 2020. [21] Ruchika Verma, Neeraj Kumar, Abhijeet Patil, Nikhil Cherian Kurian, Swapnil Rane, and Amit Sethi, “Multi-organ nuclei segmentation and classification challenge 2020,” . [22] Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, and Nasir Rajpoot, “Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images,” Medical Image Analysis, vol. 58, pp. 101563, 2019. [23] Ciresan, D.C., Gambardella, L.M., Giusti, A., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: NIPS. pp. 2852–2860 (2012) [24] Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with convolutional neural networks. In: NIPS (2014) [25] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1858
dc.description	Supervised by Mr. Tareque Mohmud Chowdhury, Co-Supervised by Mr. Tasnim Ahmed, Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	Nuclei instance segmentation is an important step for oncological diagnosis and pathology research of cancer. HE stained images are considered the gold standard for medical diagnosis. But before being used for segmentation, it is required to pre process them. There are two principle methods to preprocess them formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS). Even though FFPE is widely used, it is a time consuming process whereas FS samples can be processed very quickly. But analysis of FS-derived HE stained images can be more difficult as rapid preparation, staining, and scanning of FS sections results in degradation of image quality. Therefore, in this thesis, we explored various state of the art segmentation architectures to create a model that will segment nuclei of FS-derived HE stained images with a high quality feature extraction. Here, we have been working on a novel dataset called CryoNuSeg that contains 30 FS-sectioned images of 10 human organs. It has a benchline score of DICE 80.3 ±4.3, AJI 52.5 5.0, PQ 47.7 6.1. U-Net is the first and most prominent architecture for biomedical image segmentation. We are exploring various U-Net architectures. We have trained Triple U-NET on the dataset using binary masks in place of U-NET keeping all other parts of the instance segmentation algorithm same such as Gaussian Filtering and Watershed Post processing. The results using Triple U-NET crossed all the benchline scores. The triple U-Net architecture gives a score of DICE 80.33, AJI 67.41 and PQ 50.56. We have developed a deep learning model that performs highly accurate nuclei segmentation of FS sections despite degraded image quality for fast oncological diagnosis.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Instance segmentation, Deep Learning, Cryosectioned H&E stained images, Triple U-net, Watershed algorithm	en_US
dc.title	Nuclei Instance Segmentation of Cryosectioned H&E Stained Histological Images using Deep Learning	en_US
dc.type	Thesis	en_US