Histology Category

Nucleus subtype classification using inter-modality learning

Dec. 19, 2023—Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Y. Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2024). Nucleus subtype classification using inter-modality learning. SPIE Medical Imaging 2024 :...

Exploring shared memory architectures for end-to-end gigapixel deep learning

Dec. 19, 2023—Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios, Karthik Ramadass, Aravind Krishnan, Ruining Deng, Can Cui, Shunxing Bao, Lori A. Coburn, Yuankai Huo, Bennett A. Landman (2023). Exploring shared memory architectures for end-to-end gigapixel deep learning. MIDL 2023 short paper track Full text: NIHMSID Abstract Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development...

Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging

Dec. 10, 2021—Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Qi Yang, Xin Yu, Sophie Chiron, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo. “Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging” Medical Imaging 2022: Digital and Computational Pathology. International Society for Optics and Photonics, accepted Full text: [TBD] Abstract Multiplex immunofluorescence (MxIF) is...

Roadmap of the challenges of microstructure biophysical modeling. The main objective of this review is to propose a roadmap for development of a reliable biophysical model of diffusion in a given tissue. Discrete steps along this course are shown above, although the entire process is very much a continuous and iterative process of validation, adaptation, and application

Challenges for biophysical modeling of microstructure

Dec. 9, 2020—Ileana O. Jelescu, Marco Palombo, Francesca Bagnato, Kurt G. Schilling. “Challenges for biophysical modeling of microstructure”. (2020) Journal of Neuroscience Methods 108861. Full text: https: https://www.sciencedirect.com/science/article/pii/S0165027020302843 Abstract The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of...

A cross-platform informatics system for the Gut Cell Atlas: integrating from clinical, anatomical and histological data

Dec. 6, 2020—Shunxing Bao, Sophie Chiron, Yucheng Tang, Cody N. Heiser, Austin N. Southard-Smith, Ho Hin Lee, Marisol A. Ramirez, Yuankai Huo, Mary K. Washington, Elizabeth A. Scoville, Joseph T. Roland, Qi Liu, Ken S. Lau, Keith T. Wilson, Lori A. Coburn, Bennett A. Landman, A cross-platform informatics system for the Gut Cell Atlas: integrating from clinical,...

Different classes of methods have been used to infer tissue microstructure from single shell and multi shell DW-MRI data. The gap addressed herein is in data-driven machine learning models for multi-shell DW-MRI data.

Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE

Jan. 17, 2020—Nath V, Lyu I, Schilling KG, Parvathaneni P, Hansen CB, Huo Y, Janve VA, Gao Y, Stepniewska I, Anderson AW, Landman BA. Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2019 Oct 13 (pp. 573-581). Springer, Cham. Full text: https://arxiv.org/ftp/arxiv/papers/1907/1907.06319.pdf Abstract Intra-voxel...

A) Middle axial slice of a human brain acquired using DW-MRI. B, C) CSD FOD's of the same subject on two different scanners showing inconsistency which cannot be validated without ground truth. D) Coronal slice of a Squirrel Monkey. E, F, G, H) Right: CSD FOD's at b-value of 3000 s/mm2 Left: Ground truth reconstruction using aggregated histological structural tensors depicting a loss in precision.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Hansen CB, Hainline AE, Huo Y, Blaber JA, Lyu I, Janve V, Gao Y, Stepniewska I, Anderson AW, Landman BA. Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magnetic resonance imaging. 2019 Oct 1;62:220-7. Abstract PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance...

Diffusion MRI microstructural models in the cervical spinal cord – application, normative values, and correlations with histological analysis

Dec. 16, 2019—Kurt G. Schilling, Samantha By, Haley Feiler, Bailey Box, Kristin P. O’Grady, Atlee Witt, Bennett A. Landman, Seth A. Smith. “Diffusion MRI microstructural models in the cervical spinal cord – application, normative values, and correlations with histological analysis”. NeuroImage. doi: 10.1016/j.neuroimage.2019.116026. 2019. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31326569 Abstract Multi-compartment tissue modeling using diffusion magnetic resonance imaging has proven...

Learning 3D White Matter Microstructure from 2D Histology

Apr. 1, 2019—Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this,...

Response functions estimated using conventional methods differ from the histologically derived response functions. Kernels estimated using the highest FA voxels, the iterative calibration method, and modeled from the diffusion tensor are shown in two views. The average histologically derived response function for each monkey (across 6 z stacks) is shown (monkeys 1–3 correspond to A‐C) along with the mean (red) and mean ± standard deviation (gray) surfaces to highlight variability. Note that differences in magnitude are due to differing normalization in our work and in MRTrix3. Shape, and not magnitude, determines the final FOD shape

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain.

Mar. 16, 2019—Kurt G. Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson. “Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain”. NMR in Biomedicine. 2019 Jun;32(6):e4090. doi: 10.1002/nbm.4090. Epub 2019 Mar 25. Full text: https://www.ncbi.nlm.nih.gov/pubmed/30908803 Abstract Understanding the...