Data from: Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology
Data files
Mar 11, 2024 version files 11.10 GB
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CycleGAN_training_dataset.7z
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Pancreas_cancer_.zip
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pancreas_cancer_H_E.zip
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Pancreas_IPMN_.zip
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pancreas_IPMN_H_E.zip
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Pancreas_nomral_.zip
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pancreas_nomral_H_E.zip
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Prostate_benign_.zip
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Prostate_benigun_H_E.zip
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Prostate_cancer_.zip
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Prostate_cancer_H_E.zip
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README.md
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SkinCancer_.zip
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SkinCancer_H_E.zip
Abstract
Nondestructive pathology based on three-dimensional (3D) optical microscopy holds promise as a complement to traditional destructive hematoxylin and eosin (H&E) stained slide-based pathology by providing cellular information in high throughput manner. However, conventional techniques provided superficial information only due to shallow imaging depths. Herein, we developed open-top two-photon light sheet microscopy (OT-TP-LSM) for intraoperative 3D pathology. An extended depth of field two-photon excitation light sheet was generated by scanning a nondiffractive Bessel beam, and selective planar imaging was conducted with cameras at 400 frames/s max during the lateral translation of tissue specimens. Intrinsic second harmonic generation was collected for additional extracellular matrix (ECM) visualization. OT-TP-LSM was tested in various human cancer specimens including skin, pancreas, and prostate. High imaging depths were achieved owing to long excitation wavelengths and long wavelength fluorophores. 3D visualization of both cells and ECM enhanced the ability of cancer detection. Furthermore, an unsupervised deep learning network was employed for the style transfer of OT-TP-LSM images to virtual H&E images. The virtual H&E images exhibited comparable histological characteristics to real ones. OT-TP-LSM may have the potential for histopathological examination in surgical and biopsy applications by rapidly providing 3D information.
README: Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology
https://doi.org/10.5061/dryad.pk0p2ngwn
This dataset contains images and code that support the study entitled "Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology" published in eLife (https://doi.org/10.7554/eLife.92614.2)
Description of the data and file structure
This dataset contains OT-TP-LSM and H&E image datasets from skin (cancer), pancreas (normal, premalignant, cancer), prostate (benign, cancer). In addition, the pancreatic OT-TP-LSM and H&E images used to train cycle-consistent generational adversarial networks (CycleGAN) for virtual H&E are included.
Image dataset of two basal cell carcinoma (BCC) skin cancer used in Figure 2 are represented in this database:
SkinCancer_.zip and SkinCancer_H_E.zip
Image dataset of normal and intraductal papillary mucinous neoplasm (IPMN) pancreas used in Figure 3 are represented in this database:
Pancreas_nomral_.zip and Pancreas_nomral_H_E.zip
Pancreas_IPMN_.zip and Pancreas_IPMN_H_E.zip
Image dataset of pancreatic cancer used in Figure 4 are represented in this database:
Pancreas_cancer_.zip and Pancreas_cancer_H_E.zip
Image dataset of benign and cancer prostate used in Figure 5 are represented in this database:
Prostate_benign_.zip and Prostate_benign_H_E.zip
Prostate_cancer_.zip and Prostate_cancer_H_E.zip
Image dataset to train CycleGAN for virtual H&E used in Figure 6 are represented in this database:
CycleGAN_training_dataset.7z
The following abbreviations are used in the naming of images of the dataset:
SHG = second harmonic generation
BCC = basal cell carcinoma
H_E = hematoxylin & eosin
PI: propidium iodide
CycleGAN: cycle-consistent generative adversarial network
Description of sub-folders in database files
"Fluorescence" in SkinCancer_.zip = cell structure images after staining proflavine
"Fluorescence" in Pancreas_nomral_.zip, Pancreas_IPMN_.zip, Pancreas_cancer_.zip, Prostate_benign_.zip and Prostate_cancer_.zip = cell structure images after PI staining
"SHG" in all database = collagen fibers images within the extracellular matrix
"Second strips" in Prostate_cancer_.zip = Strip images connected laterally to the original strip images
"trainA_pancreas" in CycleGAN_training_dataset.7z = pancreas images after PI staining
"trainB_pancreas" in CycleGAN_training_dataset.7z = H&E images of pancreas
Sharing/Access information
All data in this dataset was generated for, and underlies the conclusion of the following study:
Code/Software
Code for 3D reconstruction image processing (Matlab) and virtual hematoxylin and eosin (H&E) is available on Github (https://github.com/Won-Yeong-Park/OT-TPLSM)