A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images

Abe, H., Tani, T., Mashiko, H., Kitamura, N., Miyakawa, N., Mimura, K., Sakai, K., Suzuki, W., Kurotani, T., Mizukami, H., Watakabe, A., Yamamori, T., & Ichinohe, N. (2017). 3D reconstruction of brain section images for creating axonal projection maps in marmosets. Journal of Neuroscience Methods, 286, 102–113.

Article  Google Scholar 

Alturkistani, H. A., Tashkandi, F. M., & Mohammedsaleh, Z. M. (2015). Histological Stains: A literature review and case study [Epub ahead of print]. Global Journal of Health Science, 8(3), 72–79. https://doi.org/10.5539/gjhs.v8n3p72

Article  PubMed Central  Google Scholar 

Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M. -É., Bludau, S., Bazin, P.-L., Lewis, L. B., Oros-Peusquens, A.-M., Shah, N. J., Lippert, T., Zilles, K., & Evans, A. C. (2013). BigBrain: An ultrahighresolution 3D human brain model. Science, 340(6139), 1472–1475.

Article  Google Scholar 

Atapour, N., Majka, P., Wolkowicz, I. H., Malamanova, D., Worthy, K. H., & Rosa, M. G. P. (2018). Neuronal distribution across the cerebral cortex of the marmoset monkey (Callithrix jacchus). Cerebral Cortex, 29(9), 3836–3863.

Article  Google Scholar 

Avants, B. B., Tustison, N., & Song, G. (2009). Advanced normalization tools (ANTS). Insight J, 2(365), 1–35.

Google Scholar 

Baldassarre, A., Ramsey, L. E., Siegel, J. S., Shulman, G. L., & Corbetta, M. (2016). Brain connectivity and neurological disorders after stroke. Current Opinion in Neurology, 29(6), 706–713.

Article  PubMed Central  Google Scholar 

Celaya, A., Riviere, B., & Fuentes, D. (2024). A generalized surface loss for reducing the Hausdorff distance in medical imaging segmentation. https://doi.org/10.48550/arXiv.2302.03868

Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C., Mateus, D., Peter, L., Bradley, A., Tavares, J. M. R. S., Belagiannis, V., Papa, J. P., Nascimento, J. C., Loog, M., Lu, Z., Cardoso, J. S., & Cornebise, J. (2016). The importance of skip connections in biomedical image segmentation. Deep Learning and Data Labeling for Medical Applications, 179-187.

Fröhlich, F. (2016). Chapter 7 - Microcircuits of the Neocortex. In F. Fröhlich (Ed.), Network neuroscience (pp. 85–95). Academic Press.

Chapter  Google Scholar 

Gao, X., & Chen, J. (2011). Mild traumatic brain injury results in extensive neuronal degeneration in the cerebral cortex. Journal of Neuropathology & Experimental Neurology, 70(3), 183–191.

Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–9. https://doi.org/10.5812/ijem.3505

Article  PubMed Central  Google Scholar 

Gudavalli, C., Rosten, E., Nataraj, L., Chandrasekaran, S., & Manjunath, B. S. (2024). Cimgen: Controlled image manipulation by finetuning pretrained generative models on limited data. https://arxiv.org/abs/2401.13006

Heimann, T., Van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P. M. M., Chi, Y., Cordova, A., Dawant, B. M., Fidrich, M., & Wolf, I. (2009). Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Transactions on Medical Imaging, 28(8), 1251–1265.

Article  Google Scholar 

Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211.

Article  Google Scholar 

Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2018). Image-to-image translation with conditional adversarial networks. https://doi.org/10.48550/arXiv.1611.07004

Kiwitz, K., Schiffer, C., Spitzer, H., Dickscheid, T., & Amunts, K. (2020). Deep learning networks reflect cytoarchitectonic features used in brain mapping. Scientific Reports, 10(1), 22039.

Article  PubMed Central  Google Scholar 

Kumar, D. M., Satyanarayana, D., & Prasad, M. G. (2021). MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2867–2880.

Article  Google Scholar 

Lee, B., Yamanakkanavar, N., & Choi, J. Y. (2020). Automatic segmentation of brain mri using a novel patch-wise u-net deep architecture. PLOS ONE, 15(8), 1–20. https://doi.org/10.1371/journal.pone.0236493

Article  Google Scholar 

Li, C., Zia, M. Z., Tran, Q.-H., Yu, X., Hager, G. D., & Chandraker, M. (2019). Deep supervision with intermediate concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 1828–1843.

Article  Google Scholar 

Li, R., Wang, X., Huang, G., Yang, W., Zhang, K., Gu, X., Tran, S. N., Garg, S., Alty, J., & Bai, Q. (2022). A comprehensive review on deep supervision: Theories and applications. https://doi.org/10.48550/arXiv.2207.02376

MacFarland, T. W., & Yates, J. M. (2016). Mann-whitney u test. In Introduction to nonparametric statistics for the biological sciences using R (pp. 103-132). Springer International Publishing. https://doi.org/10.1007/978-3-319-30634-6_4

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M. A., Wiesenfarth, M., Kavur, A. E., Sudre, C. H., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Rädsch, T., . . . Jäger, P. F. (2024). Metrics reloaded: Recommendations for image analysis validation. Nature Methods, 1–18.

Minshew, N. J., & Williams, D. L. (2007). The new neurobiology of autism: Cortex, connectivity, and neuronal organization. Archives of Neurology, 64(7), 945–950.

Article  PubMed Central  Google Scholar 

Müller, D., Soto-Rey, I., & Kramer, F. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes, 15(1), 210.

Article  PubMed Central  Google Scholar 

Naidich, T. P., Nimchinsky, E. A., & Pasik, P. (2013). CHAPTER 10 - Cerebral Cortex. In T. P. Naidich, M. Castillo, S. Cha, & J. G. Smirniotopoulos (Eds.), Imaging of the brain (pp. 154–173). W.B. Saunders.

Chapter  Google Scholar 

Narayanan, S. N., Bairy, L. K., & Srinivasamurthy, S. K. (2020). Determining factors for optimal neuronal and glial golgi-cox staining. Histochemistry and Cell Biology, 154(4), 431–448.

Article  Google Scholar 

Palomero-Gallagher, N., & Zilles, K. (2019). Cortical layers: Cyto-, myelo-, receptorand synaptic architecture in human cortical areas. NeuroImage, 197, 716–741.

Article  Google Scholar 

Paul, C. A., Beltz, B., & Berger-Sweeney, J. (2008). The nissl stain: A stain for cell bodies in brain sections [Epub ahead of print]. CSH Protoc, 2008, pdb.prot4805. https://doi.org/10.1101/pdb.prot4805

Peck, A. (2006). Beginning GIMP: from novice to professional. A press.

Ren, S., Wei, F., Albanie, S., Zhang, Z., & Hu, H. (2023). DeepMIM: Deep supervision for masked image modeling. https://doi.org/10.48550/arXiv.2303.08817

Rockland, K. S. (2019). What do we know about laminar connectivity? NeuroImage, 197, 772–784.

Article  Google Scholar 

Ronneberger, O., Fischer, P., Brox, T., Hornegger, J., Wells, W. M., & Frangi, A. F. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention - MICCAI, 2015, 234–241.

Google Scholar 

Rowe, J. B. (2010). Connectivity analysis is essential to understand neurological disorders. Frontiers in Systems Neuroscience, 4, 144.

Article  PubMed Central  Google Scholar 

Roy, P., Dutta, S., Dey, N., Dey, G., Chakraborty, S., & Ray, R. (2014). Adaptive thresholding: A comparative study. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 1182-1186). https://doi.org/10.1109/ICCICCT.2014.6993140

Sarraf, A., Azhdari, M., & Sarraf, S. (2021). A comprehensive review of deep learning architectures for computer vision applications. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 77(1), 1–29.

Google Scholar 

Shamir, I., Assaf, Y., & Shamir, R. (2024). Clustering the cortical laminae: In vivo parcellation. Brain Structure and Function, 229(2), 443–458. https://doi.org/10.1007/s00429-023-02748-2

Singhal, V., Chou, N., Lee, J., Yue, Y., Liu, J., Chock, W. K., Lin, L., Chang, Y.-C., Teo, E. M. L., Aow, J., Lee, H. K., Chen, K. H., & Prabhakar, S. (2024). BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nature Genetics, 56(3), 431–441. https://doi.org/10.1038/s41588-024-01664-3

Article  PubMed Central  Google Scholar 

Štajduhar, A., Lipić, T., Lončarić, S., Judaš, M., & Sedmak, G. (2023). Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture. Scientific Reports, 13(1), 5567.

Article  PubMed Central  Google Scholar 

Stephan, H., Baron, G., & Schwerdtfeger, W. K. (2012). The brain of the common marmoset (Callithrix jacchus): A stereotaxic atlas. Springer Science & Business Media.

Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15, 1–28.

Article  Google Scholar 

Tsuda, H., & Hotta, K. (2019). Cell image segmentation by integrating pix2pixs for each class. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, 1065–1073. https://doi.org/10.1109/CVPRW.2019.00139

Vanni, S., Hokkanen, H., Werner, F., & Angelucci, A. (2020). Anatomy and physiology of macaque visual cortical areas v1, v2, and v5/mt: Bases for biologically realistic models. Cerebral Cortex, 30(6), 3483–3517. https://doi.org/10.1093/cercor/bhz322

Wagstyl, K., Larocque, S., Cucurull, G., Lepage, C., Cohen, J. P., Bludau, S., Palomero-Gallagher, N., Lewis, L. B., Funck, T., Spitzer, H., Dickscheid, T., Fletcher, P. C., Romero, A., Zilles, K., Amunts, K., Bengio, Y., & Evans, A. C. (2020). BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices. PLoS Biology, 18(4), e3000678.

Article  PubMed Central  Google Scholar 

Woodward, A., Gong, R., Abe, H., Nakae, K., Hata, J., Skibbe, H., Yamaguchi, Y., Ishii, S., Okano, H., Yamamori, T., & Ichinohe, N. (2020). The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain. Brain Structure and Function, 225(4), 1225–1243.

Article  Google Scholar 

Woodward, A., Hashikawa, T., Maeda, M., Kaneko, T., Hikishima, K., Iriki, A., Okano, H., & Yamaguchi, Y. (2018). The Brain/MINDS 3D digital marmoset brain atlas. Scientific Data, 5(1), 1–12.

Article  Google Scholar 

Yeghiazaryan, V., & Voiculescu, I. (2018). Family of boundary overlap metrics for the evaluation of medical image segmentation. Journal of Medical Imaging, 5(1), 015006–015006.

Article  PubMed Central  Google Scholar 

Zeng, X., Puonti, O., Sayeed, A., Herisse, R., Mora, J., Evancic, K., Varadarajan, D., Balbastre, Y., Costantini, I., Scardigli, M., Ramazzotti, J., DiMeo, D., Mazzamuto, G., Pesce, L., Brady, N., Cheli, F., Pavone, F. S., Hof, P. R., Frost, R.,. . . Fischl, B. (2023). Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex [Epubahead of print]. bioRxiv. https://doi.org/10.1101/2023.12.06.570416

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2020). Unpaired image-to-image translation using cycle-consistent adversarial networks. https://doi.org/10.48550/arXiv.1703.10593

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