TY - GEN
T1 - Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study
AU - Gadermayr, M.
AU - Klinkhammer, B.M.
AU - Boor, P.
N1 - Conference code: 228379
Export Date: 14 December 2023
Correspondence Address: Gadermayr, M.; Salzburg University of Applied SciencesAustria; email: [email protected]
References: Barker, J., Hoogi, A., Depeursinge, A., Rubin, D.L., Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles (2016) Med. Image Anal., 30, pp. 60-71; Bentaieb, A., Hamarneh, G., Topology aware fully convolutional networks for histology gland segmentation (2016) MICCAI 2016. LNCS, 9901, pp. 460-468. , https://doi.org/10.1007/978-3-319-46723-853, Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.), pp., Springer, Cham; Gadermayr, M., Appel, V., Klinkhammer, B.M., Boor, P., Merhof, D., Which way round? A study on the performance of stain-translation for segmenting arbitrarily dyed histological images (2018) MICCAI 2018. LNCS, 11071, pp. 165-173. , https://doi.org/10.1007/978-3-030-00934-219, Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.), pp., Springer, Cham; Gadermayr, M., Dombrowski, A.K., Klinkhammer, B.M., Boor, P., Merhof, D., CNN cascades for segmenting sparse objects in gigapixel whole slide images (2019) Comput. Med. Imaging Graph., 71, pp. 40-48; Gadermayr, M., Eschweiler, D., Jeevanesan, A., Klinkhammer, B.M., Boor, P., Merhof, D., Segmenting renal whole slide images virtually without training data (2017) Comput. Biol. Med., 90, pp. 88-97; Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H., Patch-based convolutional neural network for whole slide tissue image classification (2016) Proceedings of the International Conference on Computer Vision (CVPR 2016; Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A., Image-to-image translation with conditional adversarial networks (2017) In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR; Johnson, J., Alahi, A., Fei-Fei, L., Perceptual losses for real-time style transfer and super-resolution (2016) ECCV 2016. LNCS, 9906, pp. 694-711. , https://doi.org/10.1007/978-3-319-46475-643, In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.); Macenko, M., A method for normalizing histology slides for quantitative analysis (2009) Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107-1110. , https://doi.org/10.1109/ISBI.2009.5193250, ISBI 2009), pp; Ronneberger, O., Fischer, P., Brox, T., U-Net: Convolutional networks for biomedical image segmentation (2015) MICCAI 2015. LNCS, 9351, pp. 234-241. , https://doi.org/10.1007/978-3-319-24574-428, Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.), pp., Springer, Cham; Sertel, O., Kong, J., Shimada, H., Catalyurek, U.V., Saltz, J.H., Gurcan, M.N., Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development (2009) Pattern Recognit, 42 (6), pp. 1093-1103; Zhu, J.Y., Park, T., Isola, P., Efros, A.A., Unpaired image-to-image translation using cycle-consistent adversarial networks (2017) In: Proceedings of the International Conference on Computer Vision (ICCV 2017)
PY - 2019
Y1 - 2019
N2 - Approaches relying on adversarial networks facilitate image-to-image-translation based on unpaired training and thereby open new possibilities for special tasks in image analysis. We propose a methodology to improve segmentability of histological images by making use of image-to-image translation. We generate virtual stains and exploit the additional information during segmentation. Specifically a very basic pixel-based segmentation approach is applied in order to focus on the information content available on pixel-level and to avoid any bias which might be introduced by more elaborated techniques. The results of this proof-of-concept trial indicate a performance gain compared to segmentation with the source stain only. Further experiments including more powerful supervised state-of-the-art machine learning approaches and larger evaluation data sets need to follow. © 2019, Springer Nature Switzerland AG.
AB - Approaches relying on adversarial networks facilitate image-to-image-translation based on unpaired training and thereby open new possibilities for special tasks in image analysis. We propose a methodology to improve segmentability of histological images by making use of image-to-image translation. We generate virtual stains and exploit the additional information during segmentation. Specifically a very basic pixel-based segmentation approach is applied in order to focus on the information content available on pixel-level and to avoid any bias which might be introduced by more elaborated techniques. The results of this proof-of-concept trial indicate a performance gain compared to segmentation with the source stain only. Further experiments including more powerful supervised state-of-the-art machine learning approaches and larger evaluation data sets need to follow. © 2019, Springer Nature Switzerland AG.
KW - Adversarial networks
KW - Glomeruli
KW - Histology
KW - Kidney
KW - Segmentation
KW - Tubuli
KW - Unsupervised
KW - Image enhancement
KW - Pathology
KW - Pixels
KW - Image segmentation
U2 - 10.1007/978-3-030-23937-4_5
DO - 10.1007/978-3-030-23937-4_5
M3 - Conference contribution
SN - 978-3-030-23936-7
VL - 11435 LNCS
T3 - Lecture Notes in Computer Science
SP - 38
EP - 46
BT - Digital Pathology
PB - Springer Nature
T2 - 15th European Congress on Digital Pathology, ECDP 2019
Y2 - 10 April 2019 through 13 April 2019
ER -