Abstract
Architecture distortions of glands and villi are indication of chronic inflammation. However, the “duality” nature of these two structures causes lots of ambiguity for their detection in H&E histology tissue images, especially when multiple instances are clustered together. Based on the observation that once such an object is detected for certain, the ambiguity in the neighborhood of the detected object can be reduced considerably, we propose to combine deep learning and domain knowledge in a unified framework, to simultaneously detect (the closely related) glands and villi in H&E histology tissue images. Our method iterates between exploring domain knowledge and performing deep learning classification, and the two components benefit from each other. (1) By exploring domain knowledge, the generated object proposals (to be fed to deep learning) form a more complete coverage of the true objects and the segmentation of object proposals can be more accurate, thus improving deep learning’s performance on classification. (2) Deep learning can help verify the class of each object proposal, and provide feedback to repeatedly “refresh” and enhance domain knowledge so that more reliable object proposals can be generated later on. Experiments on clinical data validate our ideas and show that our method improves the state-of-the-art for gland detection in H&E histology tissue images (to our best knowledge, we are not aware of any method for villi detection).
This research was supported in part by NSF Grant CCF-1217906, a grant of the National Academies Keck Futures Initiative (NAKFI), and NIH grant K08-AR061412-02 Molecular Imaging for Detection and Treatment Monitoring of Arthritis.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Fu, H., Qiu, G., Shu, J., Ilyas, M.: A novel polar space random field model for the detection of glandular structures. IEEE Trans. Med. Imaging 33, 764–776 (2014)
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587 (2013)
Gunduz-Demir, C., Kandemir, M., Tosun, A.B., Sokmensuer, C.: Automatic segmentation of colon glands using object-graph. Medical Image Analysis 14(1) (2010)
Haeker, M., Wu, X., Abràmoff, M.D., Kardon, R., Sonka, M.: Incorporation of regional information in optimal 3-D graph search with application for intraretinal layer segmentation of optical coherence tomography images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 607–618. Springer, Heidelberg (2007)
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 297–312. Springer, Heidelberg (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)
Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB (2007)
Naini, B.V., Cortina, G.: A histopathologic scoring system as a tool for standardized reporting of chronic (ileo) colitis and independent risk assessment for inflammatory bowel disease. Human Pathology 43, 2187–2196 (2012)
Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 115–123. Springer, Heidelberg (2012)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. In: ICLR (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, J., MacKenzie, J.D., Ramachandran, R., Chen, D.Z. (2015). Detection of Glands and Villi by Collaboration of Domain Knowledge and Deep Learning. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-24571-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24570-6
Online ISBN: 978-3-319-24571-3
eBook Packages: Computer ScienceComputer Science (R0)