Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout (2305.05006v2)

Published 8 May 2023 in eess.IV and cs.CV

Abstract: Generating realistic tissue images with annotations is a challenging task that is important in many computational histopathology applications. Synthetically generated images and annotations are valuable for training and evaluating algorithms in this domain. To address this, we propose an interactive framework generating pairs of realistic colorectal cancer histology images with corresponding glandular masks from glandular structure layouts. The framework accurately captures vital features like stroma, goblet cells, and glandular lumen. Users can control gland appearance by adjusting parameters such as the number of glands, their locations, and sizes. The generated images exhibit good Frechet Inception Distance (FID) scores compared to the state-of-the-art image-to-image translation model. Additionally, we demonstrate the utility of our synthetic annotations for evaluating gland segmentation algorithms. Furthermore, we present a methodology for constructing glandular masks using advanced deep generative models, such as latent diffusion models. These masks enable tissue image generation through a residual encoder-decoder network.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. Quiros, A. C., Murray-Smith, R., and Yuan, K., “Pathology gan: learning deep representations of cancer tissue,” arXiv preprint arXiv:1907.02644 (2019).
  2. Mahmood, F., Borders, D., Chen, R. J., Mckay, G. N., Salimian, K. J., Baras, A., and Durr, N. J., “Deep adversarial training for multi-organ nuclei segmentation in histopathology images,” IEEE Transactions on Medical Imaging 39(11), 3257–3267 (2020).
  3. Deshpande, S., Minhas, F., Graham, S., and Rajpoot, N., “Safron: Stitching across the frontier network for generating colorectal cancer histology images,” Medical Image Analysis 77, 102337 (2022).
  4. Deshpande, S., Dawood, M., Minhas, F., and Rajpoot, N., “Synclay: Interactive synthesis of histology images from bespoke cellular layouts,” Medical Image Analysis 91, 102995 (Jan. 2024).
  5. Hou, L., Agarwal, A., Samaras, D., Kurc, T., Gupta, R., and Saltz, J., “Robust histopathology image analysis: To label or to synthesize?,” 8525–8534 (06 2019).
  6. Levine, A. B., Peng, J., Farnell, D., Nursey, M., Wang, Y., Naso, J., Ren, H., Farahani, H., Chen, C., Chiu, D., Talhouk, A., Sheffield, B., Riazy, M., Ip, P., Parra-Herran, C., Mills, A., Singh, N., Tessier-Cloutier, B., Salisbury, T. D., Lee, J., Salcudean, T., Jones, S. J. M., Huntsman, D., Gilks, C., Yip, S., and Bashashati, A., “Synthesis of diagnostic quality cancer pathology images by generative adversarial networks,” The Journal of Pathology 252 (2020).
  7. McGaghie, W. C., “Medical education research as translational science,” Science Translational Medicine 2(19), 19cm8–19cm8 (2010).
  8. He, J., Baxter, S., Xu, J., Xu, J., Zhou, X., and Zhang, K., “The practical implementation of artificial intelligence technologies in medicine,” Nature Medicine 25 (01 2019).
  9. Senaras, C., Niazi, M. K. K., Sahiner, B., Pennell, M. P., Tozbikian, G., Lozanski, G., and Gurcan, M. N., “Optimized generation of high-resolution phantom images using cgan: Application to quantification of ki67 breast cancer images,” PloS one 13(5), e0196846 (2018).
  10. Deshpande, S., Minhas, F., and Rajpoot, N., “Train small, generate big: Synthesis of colorectal cancer histology images,” in [Simulation and Synthesis in Medical Imaging ], Burgos, N., Svoboda, D., Wolterink, J. M., and Zhao, C., eds., 164–173, Springer International Publishing, Cham (2020).
  11. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B., “High-resolution image synthesis with latent diffusion models,” in [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ], (2022).
  12. Esser, P., Rombach, R., and Ommer, B., “Taming transformers for high-resolution image synthesis,” in [Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ], 12873–12883 (June 2021).
  13. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I., “Zero-shot text-to-image generation,” in [Proceedings of the 38th International Conference on Machine Learning ], Meila, M. and Zhang, T., eds., Proceedings of Machine Learning Research 139, 8821–8831, PMLR (18–24 Jul 2021).
  14. Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K., “Spatial transformer networks,” in [NIPS ], (2015).
  15. Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A., “Image-to-image translation with conditional adversarial networks,” in [2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ], 5967–5976 (2017).
  16. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y., “Generative adversarial nets,” in [Advances in neural information processing systems ], 2672–2680 (2014).
  17. van den Oord, A., Vinyals, O., and Kavukcuoglu, K., “Neural discrete representation learning,” in [Proceedings of the 31st International Conference on Neural Information Processing Systems ], NIPS’17, 6309–6318, Curran Associates Inc., Red Hook, NY, USA (2017).
  18. Ho, J., Jain, A., and Abbeel, P., “Denoising diffusion probabilistic models,” in [Advances in Neural Information Processing Systems ], Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H., eds., 33, 6840–6851, Curran Associates, Inc. (2020).
  19. Ronneberger, O., Fischer, P., and Brox, T., “U-net: Convolutional networks for biomedical image segmentation,” in [International Conference on Medical image computing and computer-assisted intervention ], 234–241, Springer (2015).
  20. Ashual, O. and Wolf, L., “Specifying object attributes and relations in interactive scene generation,” 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , 4560–4568 (2019).
  21. Graham, S., Chen, H., Gamper, J., Dou, Q., Heng, P.-A., Snead, D., Tsang, Y. W., and Rajpoot, N., “Mild-net: minimal information loss dilated network for gland instance segmentation in colon histology images,” Medical image analysis 52, 199–211 (2019).
  22. Sirinukunwattana, K., Pluim, J. P., Chen, H., Qi, X., Heng, P.-A., Guo, Y. B., Wang, L. Y., Matuszewski, B. J., Bruni, E., Sanchez, U., Böhm, A., Ronneberger, O., Cheikh, B. B., Racoceanu, D., Kainz, P., Pfeiffer, M., Urschler, M., Snead, D. R., and Rajpoot, N. M., “Gland segmentation in colon histology images: The glas challenge contest,” Medical Image Analysis 35, 489–502 (jan 2017).
  23. Sirinukunwattana, K., Snead, D. R. J., and Rajpoot, N. M., “A stochastic polygons model for glandular structures in colon histology images,” IEEE Transactions on Medical Imaging 34(11), 2366–2378 (2015).
  24. Kingma, D. and Ba, J., “Adam: A method for stochastic optimization,” International Conference on Learning Representations (12 2014).
  25. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S., “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in [Advances in neural information processing systems ], 6626–6637 (2017).
  26. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z., “Rethinking the inception architecture for computer vision,” in [Proceedings of the IEEE conference on computer vision and pattern recognition ], 2818–2826 (2016).
  27. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L., “Imagenet: A large-scale hierarchical image database,” in [2009 IEEE conference on computer vision and pattern recognition ], 248–255, Ieee (2009).
  28. Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M., Kaus, M. R., Haker, S. J., Wells III, W. M., Jolesz, F. A., and Kikinis, R., “Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports,” Academic radiology 11(2), 178–189 (2004).
  29. Bradski, G., “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools (2000).
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now