Apr 16, 2024

Public workspaceCODA (part 1): setting up environment and preparing sample dataset | HuBMAP | JHU-TMC V.2

DOI
dx.doi.org/10.17504/protocols.io.q26g71rpkgwz/v2
  • 1Johns Hopkins University;
  • 2Johns Hopkins Medicine
Kyu Sang Han
Open access
DOI: dx.doi.org/10.17504/protocols.io.q26g71rpkgwz/v2
Protocol Citation: Kyu Sang Han, Pei-Hsun Wu, Joel Sunshine, Sashank Reddy, Ashley Kiemen, Denis Wirtz 2024. CODA (part 1): setting up environment and preparing sample dataset | HuBMAP | JHU-TMC. protocols.io https://dx.doi.org/10.17504/protocols.io.q26g71rpkgwz/v2Version created by Kyu Sang Han
Manuscript citation:
A.M. Braxton, A.L. Kiemen, M.P. Grahn, A. Forjaz, J. Parksong, J.M. Babu, J. Lai, L. Zheng, N. Niknafs, L. Jiang, H. Cheng, Q. Song, R. Reichel, S. Graham, A.I. Damanakis, C.G. Fischer, S. Mou, C. Metz, J. Granger, X.-D. Liu, N. Bachmann, Y. Zhu, Y.Z. Liu, C. Almagro-Pérez, A.C. Jiang, J. Yoo, B. Kim, S. Du, E. Foster, J.Y. Hsu, P.A. Rivera, L.C. Chu, D. Liu, E.K. Fishman, A. Yuille, N.J. Roberts, E.D. Thompson, R.B. Scharpf, T.C. Cornish, Y. Jiao, R. Karchin, R.H. Hruban, P.-H. Wu, D. Wirtz, and L.D. Wood, “3D genomic mapping reveals multifocality of human pancreatic precancers”, Nature (2024)

A.L. Kiemen, A. Forjaz, R. Sousa, K. Sang Han, R.H. Hruban, L.D. Wood, P.H. Wu, and D. Wirtz, “High-resolution 3D printing of pancreatic ductal microanatomy enabled by serial histology”, Advanced Materials Technologies 9, 2301837 (2024)

T. Yoshizawa, J. W. Lee, S.-M. Hong, D.J. Jung, M. Noe, W. Sbijewski, A. Kiemen, P.H, Wu, D. Wirtz, R.H. Hruban, L.D. Wood, and K. Oshima. “Three-dimensional analysis of ductular reactions and their correlation with liver regeneration and fibrosis”, Virchows Archiv (2023).

A.L. Kiemen, A.I. Damanakis, A.M. Braxton, J. He, D. Laheru, E.K. Fishman, P. Chames, C. Almagro Perez, P.-H. Wu, D. Wirtz, L.D. Wood, and R. Hruban, “Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer”, Med 4, 75-91 (2023)

A. Kiemen, Y. Choi, A. Braxton, C. Almagro Perez, S. Graham, M. Grahm, N., N. Roberts, L. Wood, P. Wu, R. Hruban, and D. Wirtz, “Intraparenchymal metastases as a cause for local recurrence of pancreatic cancer”, Histopathology 82: 504-506 (2022)

A.L. Kiemen, A.M. Braxton, M.P. Grahn, K.S. Han, J.M. Babu, R. Reichel, A.C. Jiang, B. Kim, J. Hsu, F. Amoa, S. Reddy, S.-M. Hong, T.C. Cornish, E.D. Thompson, P. Huang, L.D. Wood, R.H. Hruban, D. Wirtz and P.H. Wu, “CODA: quantitative 3D reconstruction of large tissues at cellular resolution”, Nature Methods 19: 1490-1499 (2022)

K.S.Han, I. Sander, J. Kumer, E. Resnick, C. Booth, B. Starich, J. Walston, A.L. Kiemen, S. Reddy, C. Joshu, J. Sunshine, D. Wirtz, P.-H. Wu "The microanatomy of human skin in aging." bioRxiv (2024): 2024-04.
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: April 16, 2024
Last Modified: April 16, 2024
Protocol Integer ID: 98298
Keywords: CODA, deeplearning, tissuelabeling, segmentation
Funders Acknowledgement:
Institute of Arthritis and Musculoskeletal and Skin Diseases
Grant ID: U54AR081774
National Cancer Institute
Grant ID: U54CA143868
Abstract
CODA workflow part 1. setting up environment and preparing dataset
Software requirements
Software requirements
Download Source code
Download Source code
Codes are available at the following GitHub: CODA Github Repository
Download Sample dataset
Download Sample dataset
Here, we discuss application to a sample dataset “lungs”, containing 150 serial histological images. Download the sample dataset (serial images and sample annotations) here: Lung Sample Dataset on Google Drive
Images are .ndpi format and were scanned at 20x magnification (approximately 0.5 micron / pixel resolution), spaced 10 micron apart. Save the images in a local drive folder (e.g. \\Users\Ashley\Documents\lungs).
Filenames for each image should be created such that tissue sections are read consecutively by Matlab. Therefore, include zero-padding in numerical indices.

CORRECT FILENAMES: lungs_001.ndpi, lungs_002.ndpi, ..., lungs_011.ndpi
INCORRECT FILENAMES (no zero padding): lungs_1.ndpi, lungs_2.ndpi, ..., lungs_11.ndpi
Create downsampled copies of high-resolution images
Create downsampled copies of high-resolution images
The function create_downsampled_tif_images will create downsampled copies of the .ndpi files by directly loading each high-resolution images in tiles and down sampling it to the desired pixel resolutions.
First, decide the resolution of the images you want to create. Here, we create images of 1 micron / pixel, 2 microns / pixel, and 10 micron / pixel resolution:

ds=[1 2 10];
Next, decide on the name of the output folders for each of the downsampled images you create. Here, we will save the images downsampled to 1 micron / pixel in a folder named “10x”, the images downsampled to 2 micron / pixel in a folder named “5x,” and the images downsampled to 10 micron / pixel in a folder named “1x.”

subfolders=["10x" "5x" "1x"];
Finally, call the function: create_downsampled_tif_images(pth,ds,subfolders);
Using this function, you will make two subfolders within the original folder containing the .ndpi images. One subfolder named “10x” containing the 20x images downsampled by a factor of 2. The other subfolder named “1x” containing the 20x images downsampled by a factor of 20. Most calculations will be performed on these tif images. Note: here we use 10x and 1x for example, but other resolutions could be created as desired.

pth10x=[pth,'10x'];
pth1x=[pth,'1x'];
**Note: If this code fails due to memory constraints on your computer, try python Openslide.