Abstract
We present a novel method for quantifying the microscopic structure of brain tissue. It is based on the automated recognition of interpretable features obtained by analyzing the shapes of cells. This contrasts with prevailing methods of brain anatomical analysis in two ways. First, contemporary methods use gray-scale values derived from smoothed version of the anatomical images, which dissipated valuable information from the texture of the images. Second, contemporary analysis uses the output of black-box Convolutional Neural Networks, while our system makes decisions based on interpretable features obtained by analyzing the shapes of individual cells. An important benefit of this open-box approach is that the anatomist can understand and correct the decisions made by the computer. Our proposed system can accurately localize and identify existing brain structures. This can be used to align and coregistar brains and will facilitate connectomic studies for reverse engineering of brain circuitry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arthur, D., Vassilvitskii, S., et al.: k-means++: the advantages of careful seeding. In: Soda, vol. 7, pp. 1027–1035 (2007)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Braitenberg, P.D.V.: On the texture of brains. In: Heidelberg Science Library (1977). https://api.semanticscholar.org/CorpusID:10281267
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016). https://github.com/dmlc/xgboost
Chen, Y., McElvain, L.E., Tolpygo, A.S., Ferrante, D., Friedman, B., Mitra, P.P., Karten, H.J., Freund, Y., Kleinfeld, D.: An active texture-based digital atlas enables automated mapping of structures and markers across brains. Nat. Methods 16(4), 341–350 (2019)
Coifman, R.R., et al.: Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl. Acad. Sci. 102(21), 7426–7431 (2005)
Franklin, K.B., Paxinos, G.: Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates, Compact: The Coronal Plates and Diagrams. Academic Press, Cambridge (2019)
Štajduhar, A., Lipić, T., Lončarić, S., Judaš, M., Sedmak, G.: Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture. Sci. Rep. 13 (2023). https://api.semanticscholar.org/CorpusID:257928559
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Qian, K., Qiao, L., Friedman, B., O’Donnell, E., Kleinfeld, D., Freund, Y. (2024). Towards Explainable Automated Neuroanatomy. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham. https://doi.org/10.1007/978-3-031-72384-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-031-72384-1_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72383-4
Online ISBN: 978-3-031-72384-1
eBook Packages: Computer ScienceComputer Science (R0)