Abstract
Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells.
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References
Amunts, K., Schleicher, A., Zilles, K.: Cytoarchitecture of the cerebral cortex-more than localization. Neuroimage 37(4), 1061–1065 (2007)
Bankhead, P., et al.: Qupath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 1–7 (2017)
Chevalier, G.: Make smooth predictions by blending image patches, such as for image segmentation (2017). https://github.com/Vooban/Smoothly-Blend-Image-Patches
Corain, L., Grisan, E., Graïc, J.-M., Carvajal-Schiaffino, R., Cozzi, B., Peruffo, A.: Multi-aspect testing and ranking inference to quantify dimorphism in the cytoarchitecture of cerebellum of male, female and intersex individuals: a model applied to bovine brains. Brain Struct. Funct. 225(9), 2669–2688 (2020). https://doi.org/10.1007/s00429-020-02147-x
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
García-Cabezas, M.Á., John, Y.J., Barbas, H., Zikopoulos, B.: Distinction of neurons, glia and endothelial cells in the cerebral cortex: an algorithm based on cytological features. Front. Neuroanat. 10, 107 (2016)
Graham, S., et al.: Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019)
Graïc, J.M., Peruffo, A., Corain, L., Finos, L., Grisan, E., Cozzi, B.: The primary visual cortex of cetartiodactyls: organization, cytoarchitectonics and comparison with perissodactyls and primates. Brain Struct. Funct. 227(4), 1195–1225 (2022)
Greenwald, N.F., et al.: Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40(4), 555–565 (2022)
IJsseldijk, L.L., Brownlow, A.C., Mazzariol, S.: Best practice on cetacean post mortem investigation and tissue sampling. Jt. ACCOBAMS ASCOBANS Doc, pp. 1–73 (2019)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Schmidt, U., Weigert, M., Broaddus, C., Myers, G.: Cell detection with star-convex polygons. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 265–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_30
Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Wu, H., Souedet, N., Jan, C., Clouchoux, C., Delzescaux, T.: A general deep learning framework for neuron instance segmentation based on efficient unet and morphological post-processing. Comput. Biol. Med. 150, 106180 (2022)
Xing, F., Yang, L.: Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE Rev. Biomed. Eng. 9, 234–263 (2016)
Zack, G.W., Rogers, W.E., Latt, S.A.: Automatic measurement of sister chromatid exchange frequency. J. Histochem. Cytochem. 25(7), 741–753 (1977)
Zhou, Y., Onder, O.F., Dou, Q., Tsougenis, E., Chen, H., Heng, P.-A.: CIA-Net: robust nuclei instance segmentation with contour-aware information aggregation. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 682–693. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_53
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Vadori, V., Peruffo, A., Graïc, JM., Finos, L., Corain, L., Grisan, E. (2024). NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_46
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