Classification of textures in colorectal cancer histology. Each example is a 150 x 150 x 3 RGB image of one of 8 classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('colorectal_histology', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/colorectal_histology-2.0.0.png" alt="Visualization" width="500px">

Data Description "NCT-CRC-HE-100K"

• This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.
• All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (http://ieeexplore.ieee.org/abstract/document/5193250/, DOI 10.1109/ISBI.2009.5193250).
• Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).
• These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.

Ethics statement "NCT-CRC-HE-100K"

All experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to Niels Halama and Jakob Nikolas Kather; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017-806R-MA, granted to Alexander Marx and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).

Data set "CRC-VAL-HE-7K"

This is a set of 7180 image patches from N=50 patients with colorectal adenocarcinoma (no overlap with patients in NCT-CRC-HE-100K). It can be used as a validation set for models trained on the larger data set. Like in the larger data set, images are 224x224 px at 0.5 MPP. All tissue samples were provided by the NCT tissue bank, see above for further details and ethics statement.

Data set "NCT-CRC-HE-100K-NONORM"

This is a slightly different version of the "NCT-CRC-HE-100K" image set: This set contains 100,000 images in 9 tissue classes at 0.5 MPP and was created from the same raw data as "NCT-CRC-HE-100K". However, no color normalization was applied to these images. Consequently, staining intensity and color slightly varies between the images. Please note that although this image set was created from the same data as "NCT-CRC-HE-100K", the image regions are not completely identical because the selection of non-overlapping tiles from raw images was a stochastic process.

General comments

Please note that the classes are only roughly balanced. Classifiers should never be evaluated based on accuracy in the full set alone. Also, if a high risk of training bias is excepted, balancing the number of cases per class is recommended.

Colon Histology

## Overview

Colon Histology is a dataset for classification tasks - it contains Cells annotations for 560 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Invasive Ductal Carcinoma (IDC) is the most common subtype of all breast cancers. To assign an aggressiveness grade to a whole mount sample, pathologists typically focus on the regions which contain the IDC. As a result, one of the common pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide. Dataset Description The original dataset consisted of 162 whole mount slide images of Breast Cancer (BCa) specimens scanned at 40x. From that, 277,524 patches of size 50 x 50 were extracted (198,738 IDC negative and 78,786 IDC positive). Each patch’s file name is of the format: u_xX_yY_classC.png — > example 10253_idx5_x1351_y1101_class0.png Where u is the patient ID (10253_idx5), X is the x-coordinate of where this patch was cropped from, Y is the y-coordinate of where this patch was cropped from, and C indicates the class where 0 is non-IDC and 1 is IDC.

We present a large-scale dataset of 350 histologic samples of seven different canine cutaneous tumors. All samples were obtained through surgical resection due to neoplastic indicators and were selected retrospectively from the biopsy archive of the Institute for Veterinary Pathology of the Freie Universität Berlin according to sufficient tissue preservation and presence of characteristic histologic features for the corresponding tumor subtypes. Samples were stained with a routine Hematoxylin & Eosin dye and digitized with two Leica linear scanning systems at a resolution of 0.25 um/pixel. Together with the 350 whole slide images, we provide a database consisting of 12,424 polygon annotations for six non-neoplastic tissue classes (epidermis, dermis, subcutis, bone, cartilage, and a joint class of inflammation and necrosis) and seven tumor classes (melanoma, mast cell tumor, squamous cell carcinoma, peripheral nerve sheath tumor, plasmacytoma, trichoblastoma, and histiocytoma).

The polygon annotations were generated using the open source software SlideRunner (https://github.com/DeepPathology/SlideRunner). Within SlideRunner, users can view whole slide images (WSIs) and zoom through their magnification levels. Using multiple clicks or click-and-drag, the pathologist annotated polygons for 13 classes (epidermis, dermis, subcutis, bone, cartilage, a joint class of inflammation and necrosis, melanoma, mast cell tumor, squamous cell carcinoma, peripheral nerve sheath tumor, plasmacytoma, trichoblastoma, and histiocytoma) on 287 WSIs. The remaining WSIs were annotated by three medical students in their 8th semester supervised by the leading pathologist who later reviewed these annotations for correctness and completeness.

Due to the large size of the dataset and the extensive annotations, it provides a good basis for segmentation and classification algorithms based on supervised learning. Previous work [1-4] has shown, that due to various homologies between the species, canine cutaneous tissue can serve as a model for human samples. Prouteau et al. have published an extensive comparison of the two species especially for cutaneous tumors and include homologies between canine and human oncology regarding "clinical and histological appearance, biological behavior, tumor genetics, molecular pathways and targets, and response to therapies" [1]. Ranieri et al. highlight that pet dogs and humans share many environmental risk factors and show the highest risk for cancer development at similar points of time respective to their life spans [2]. Both, Ranieri et al. and Pinho et al. highlight the potential of using insights from experiments on canine samples for developing human cancer treatments [2,3]. From a technical perspective, Aubreville et al. have shown that canine samples can be used to aid human cancer research through the use of transfer learning methods [4].

Potential users of the dataset can load the SQLite database into their custom installation of SlideRunner and adapt or extend the database with custom annotations. Furthermore, we converted the annotations to the COCO JSON format, which is commonly used by computer scientists for training neural networks. Its pixel-level annotations can be used for supervised segmentation algorithms as opposed to datasets that only provide clinical data on slide level.

References

1. Prouteau, Anaïs, and Catherine André. "Canine melanomas as models for human melanomas: Clinical, histological, and genetic comparison." Genes 10.7 (2019): 501. https://doi.org/10.3390/genes10070501
2. Ranieri, G., et al. "A model of study for human cancer: Spontaneous occurring tumors in dogs. Biological features and translation for new anticancer therapies." Critical reviews in oncology/hematology 88.1 (2013): 187-197. https://doi.org/10.1016/j.critrevonc.2013.03.005
3. Pinho, Salomé S., et al. "Canine tumors: a spontaneous animal model of human carcinogenesis." Translational Research 159.3 (2012): 165-172. https://doi.org/10.1016/j.trsl.2011.11.005
4. Aubreville, Marc, et al. "A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research." Scientific data 7.1 (2020): 1-10. https://doi.org/10.1038/s41597-020-00756-z

uCT and histology datasetsGeneral informationThe rat was in the oestrus phase of its cycle. All slices are along the transverse plane. The organ was stained with PhosphoTungstic Acid (PTA) and the histology slices used Hematoxylin and Eosin (HE) stain.ContentsThe AWA015_PTA_1_Rec_Trans.zip archive contains the original uCT dataset of the full rat uterus. The organ was stained with PTA. The archive contains the .bmp files and the log file.The AWA015_PTA_1_Rec_Trans_muscle_segmentation.zip archive contains the segmentation masks from the full rat uterus slices (png format).The AWA015_PTA_2_Cvx_Rec_Trans.zip archive contains the original uCT dataset of a segment located in the cervix of the rat uterus. The archive contains the .bmp files and the log file.The AWA015_PTA_2_Cev_Rec_Trans.zip archive contains the original uCT dataset of a segment located near the cervix of the left horn of the rat uterus. The archive contains the .bmp files and the log file.The AWA015_PTA_2_Cen_Rec_Trans.zip archive contains the original uCT dataset of a segment located in the centre of the left horn of the rat uterus. The archive contains the .bmp files and the log file.The AWA015_PTA_2_Ova_Rec_Trans.zip archive contains the original uCT dataset of a segment located near the ovaries of the left horn of the rat uterus. The archive contains the .bmp files and the log file.The AWA015_PTA_2_Ova_Rec_Trans_muscle_segmentation.zip archive contains the segmentation masks from the slices of the segment located near the ovaries of the left horn of the rat uterus (png format). The segmentation masks have two labels, one for the circumferential muscles and two for the longitudinal muscles.The downsampled.zip archive contains the downsampled versions of the AWA015_PTA_1_Rec_Trans and AWA015_PTA_2_Ova_Rec_Trans images (png format) as nii.gz archives (NIfTI format) as well as the muscle segmentation masks (png format) as nii.gz archives and downsampling log files. The images were downsampled by a factor of 4 relative to the original datasets. The segmentation masks of AWA015_PTA_2_Ova_Rec_Trans have two labels, one for the circumferential muscles and two for the longitudinal muscles.The AWA015_histology.nii.gz archive contains the histology slices (png format) of different locations (cervix, cervical end, centre, and ovarian end of the right horn in that order) in the rat uterus. The slices were stained with Hematoxylin and Eosin (HE) stain.The AWA015_histology_muscle_segmentation.nii.gz archive contains the masks of the muscle segmentation of the histology slices (png format).

i3S Annotated Datasets on Digital Pathology

WELCOME

In an effort to contribute and push forward the field of Digital Pathology, Ipatimup and INEB, two major research institutions in Portugal, have joined forces in the construction of histology datasets to support grand Challenges on automatic classification of tissue malignancy. The researchers/pathologists responsible for the datasets are:

António Polónia (MD), Ipatimup/i3S

Catarina Eloy (MD, PhD), Ipatimup/i3S

Paulo Aguiar (PhD), INEB/i3S

This specific page refers to the Grand Challenge on Breast Cancer Histology images, or BACH Challenge

THE BACH CHALLENGE DATASET

ICIAR 2018 - Grand Challenge on Breast Cancer Histology images [Challenge organized by Teresa Araújo, Guilherme Aresta, António Polónia, Catarina Eloy and Paulo Aguiar]

For detailed information visit: https://iciar2018-challenge.grand-challenge.org/home/

THIS DATASET IS PUBLICALLY AVAILABLE UNDER A CREATIVE COMMONS CC BY-NC-ND LICENSE (ATTRIBUTION-NONCOMMERCIAL-NODERIVS) ESSENCIALLY, YOU ARE GRANTED ACCESS TO THE DATASET FOR USE IN YOUR RESEARCH AS LONG AS YOU CREDIT OUR WORK/PUBLICATIONS(*), BUT YOU CANNOT CHANGE THEM IN ANY WAY OR USE THEM COMMERCIALLY

(*) Aresta, Guilherme, et al. "BACH: Grand challenge on breast cancer histology images." Medical image analysis (2019).

(*) Araújo, Teresa, et al. "Classification of breast cancer histology images using convolutional neural networks." PloS one 12.6 (2017): e0177544.

(*) Fondón, Irene, et al. "Automatic classification of tissue malignancy for breast carcinoma diagnosis." Computers in biology and medicine 96 (2018): 41-51.

Content

This data set represents a collection of textures in histological images of human colorectal cancer. It contains two files:

"Kather_texture_2016_image_tiles_5000.zip": a zipped folder containing 5000 histological images of 150 * 150 px each (74 * 74 µm). Each image belongs to exactly one of eight tissue categories (specified by the folder name).

"Kather_texture_2016_larger_images_10.zip": a zipped folder containing 10 larger histological images of 5000 x 5000 px each. These images contain more than one tissue type.

Image format

All images are RGB, 0.495 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x. Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors) from our pathology archive (Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).

Ethics statement

All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA). The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples. All experiments were carried out in accordance with the approved guidelines and with the Declaration of Helsinki.

More information / data usage

For more information, please refer to the following article. Please cite this article when using the data set.

Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zollner F: Multi-class texture analysis in colorectal cancer histology (2016), Scientific Reports (in press)

Contact

For questions, please contact: Dr. Jakob Nikolas Kather http://orcid.org/0000-0002-3730-5348 ResearcherID: D-4279-2015

Tissue samples are collected from stranded marine mammals in the Southeastern United States. These tissue samples are examined histologically and evaluated to identify diseases, parasites, and other factors that may result in morbidity and mortality of marine mammals. These data document the different types of diseases or other health effects seen in stranded marine mammals.

The Histology And Cytology Market report segments the industry into By Type Of Examination (Histology, Cytology), By Test Type (Microscopy Tests, Molecular Genetics Tests, Flow Cytomtery), By End User (Hospitals And Clinics, Academic And Research Institutes, Other End Users), and Geography (North America, Europe, Asia-Pacific, Middle East And Africa, South America). Get five years of historic data and five-year forecasts.

Dataset

This dataset was created by RahulKumar

Contents

Fresh frozen breast cancer H&E tissue images collected and annotated by the International Cancer Genome Consortium (ICGC), that included the BASIS collaboration. Associated with whole genome sequence data as originally described by Nik-Zainal et al, Nature, 2016 (DOI: 10.1038/nature17676) and deposited with ID EGAS00001001178

BreAst Cancer Histology (BACH) Dataset: Grand Challenge on Breast Cancer Histology images

  Description

The dataset is composed of Hematoxylin and eosin (H&E) stained breast histology microscopy images. Microscopy images are labelled as normal, benign, in situ carcinoma or invasive carcinoma according to the predominant cancer type in each image. The annotation was performed by two medical experts and images where there was disagreement were discarded. Images have the… See the full description on the dataset page: https://huggingface.co/datasets/1aurent/BACH.

Data Description This is a 2 million set of non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human breast cancer tumor tissue.

The anonymized dataset comes from a cohort of BC patients from the A. C. Camargo Cancer Center (ACCCC, N = 504). All patients were treated for breast cancer at the ACCCC between 2019 and 2021. As part of their diagnosis, in HER2 IHC score 2+ cases, patients' HER2 status was determined following the ASCO guidelines updated in 2018, with visual evaluation of IHC assay and either a FISH or DDISH test. All cases with metastasis or neoadjuvant treatment were excluded.

A total of 426 H&E stained high resolution images (40x magnification) were scanned from biopsy and resection tissue samples with a Leica Aperio AT2 scanner. Ethical approval of the ACCCC study was given by the ethics committee of the Fundação Antônio Prudente. We divided the cases into the following 3 groups according to the results of the IHC and ISH tests: HER2-negative, HER2-low and HER2-high.

The slides were divided into 256 px x 256 px tiles at 0.5 um/pixel magnification. Then, we used a custom trained ConvNext-tiny neural network to only include tiles from the tumor region and its environment, generating a total of 2051877 image patches.

A sample is considered her2-negative with an IHC score of 0; her2-low with an IHC score of 1+ or an IHC score of 2+ with a negative ISH-based test result, and her2-high with an IHC score of 2+ with a positive ISH-based test or an IHC score of 3+.

The accompanying code used for training the models is available at https://github.com/tojallab/wsi-mil

This data set contains the histological images as reported in our manuscript "Tricuspid valve maladaptation in sheep with biventricular heart failure: The posterior and septal leaflets"

Summary:

This repository includes data related to the ERC Starting Grant project 677697: "Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies" (BUNGEE-TOOLS). It includes: (a) Dense histological sections from five human hemispheres with manual delineations of >300 brain regions; (b) Corresponding ex vivo MRI scans; (c) Dissection photographs; (d) A spatially aligned version of the dataset; (e) A probabilistic atlas built from the hemispheres; and (f) Code to apply the atlas to automated segmentation of in vivo MRI scans.

More detailed description on what this dataset includes:

Data files and Python code for Bayesian segmentation of human brain MRI based on a next-generation, high-resolution histological atlas: "Next-Generation histological atlas for high-resolution segmentation of human brain MRI" A Casamitjana et al., in preparation. This repository contains a set of zip files, each corresponding to one directory. Once decompressed, each directory has a readme.txt file explaining its contents. The list of zip files / compressed directories is:

• 3dAtlas.zip: nifti files with summary imaging volumes of the probabilistic atlas.

• BlockFacePhotoBlocks.zip: nifti files with the blackface photographs acquired during tissue sectioning, reconstructed into 3D volumes (in RGB).

• Histology.zip: jpg files with the LFB and H&E stained sections.

• HistologySegmentations.zip: 2D nifti files with the segmentations of the histological sections.

• MRI.zip: ex vivo T2-weighted MRI scans and corresponding FreeSurfer processing files

• SegmentationCode.zip: contains the the Python code and data files that we used to segment brain MRI scans and obtain the results presented in the article (for reproducibility purposes). Note that it requires an installation of FreeSurfer. Also, note that the code is also maintained in FreeSurfer (but may not produce exactly the same results): https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation

• WholeHemispherePhotos.zip: photographs of the specimens prior to dissection

• WholeSlicePhotos.zip: photographs of the tissue slabs prior to blocking.

We also note that the registered images for the five cases can be found in GitHub: https://github.com/UCL/BrainAtlas-P41-16 https://github.com/UCL/BrainAtlas-P57-16 https://github.com/UCL/BrainAtlas-P58-16 https://github.com/UCL/BrainAtlas-P85-18 https://github.com/UCL/BrainAtlas-EX9-19
These registered images can be interactively explored with the following web interface: https://github-pages.ucl.ac.uk/BrainAtlas/#/atlas

The global histology and cytology market is experiencing robust growth, driven by several key factors. The increasing prevalence of chronic diseases such as cancer, which necessitates extensive diagnostic testing, is a major catalyst. Technological advancements, including the development of automated systems, digital pathology, and AI-powered image analysis, are significantly improving diagnostic accuracy and efficiency, thereby boosting market demand. Furthermore, the rising geriatric population, with its associated higher susceptibility to chronic illnesses, is fueling the need for more sophisticated diagnostic tools. The market is also witnessing a growing adoption of point-of-care testing and telehealth solutions, enabling faster diagnosis and treatment, particularly in remote areas. This trend enhances accessibility and efficiency within healthcare systems, positively impacting market expansion. Despite these positive factors, the market faces some challenges. High equipment costs and the need for skilled professionals to operate and interpret results can hinder widespread adoption, especially in resource-constrained settings. Strict regulatory requirements and reimbursement policies in different regions also influence market growth. However, ongoing technological innovations and collaborative efforts between healthcare providers and technology companies are addressing these challenges, leading to improved affordability and accessibility of histology and cytology services. The market segmentation reveals significant opportunities within specialized testing areas and emerging markets, paving the way for sustained growth throughout the forecast period. Competitive landscape analysis shows key players like Abbott Laboratories, BD, Danaher, Roche, Hologic, Sysmex, Thermo Fisher Scientific, and Trivitron Healthcare vying for market share through innovation, strategic partnerships, and acquisitions.

P = 0.0125 vs. untreated WT mice for histological differences.*P = 0.0205 vs. untreated PDK1 mice for histological differences.Wild-type (WT) and MMTV-PDK1 transgenic mice (PDK1) were fed either standard rodent chow or chow supplemented with 0.005% (w/w) GW501516 (GW). GW501516 treatment produced a significant change in the percentage of adenosquamous/squamous carcinomas. There were no significant differences in tumor multiplicity between groups.

# # # Machine Learning Model for identifying Cell Nuclei from Histology Images

Machine learning model for identifying cell nuclei from histology images. The model having the ability to generalize across a variety of lighting conditions, cell types, magnifications, and imaging modalities.Imagine speeding up research for almost every disease, from lung cancer and heart disease to rare disorders. The Data Science Bowl offers to data scientist / practitioner a most ambitious mission i.e. create an algorithm to automate nucleus detection & create an algorithm to detect all non overlapped nuclei from the given test data i.e. It should have the capability for instance segmentation. We’ve all seen people suffer from diseases like cancer, heart disease, chronic obstructive pulmonary disease, Alzheimer’s, and diabetes. Many have seen their loved ones pass away. Think how many lives would be transformed if cures came faster. By automating nucleus detection, you could help unlock cures faster—from rare disorders to the common cold

# ## Why nuclei?

Identifying the cells’ nuclei is the starting point for most analyses because most of the human body’s 30 trillion cells contain a nucleus full of DNA, the genetic code that programs each cell. Identifying nuclei allows researchers to identify each individual cell in a sample, and by measuring how cells react to various treatments, the researcher can understand the underlying biological processes at work.By participating, teams will work to automate the process of identifying nuclei, which will allow for more efficient drug testing, shortening the 10 years it takes for each new drug to come to market

Acknowledgements

The success and final outcome of this project required a lot of guidance and assistance from many people and I am extremely privileged to have got this all along the completion of my project. All that I have done is only due to such supervision and assistance and I would not forget to thank them.I owe my deep gratitude to our project guide C - DAC Noida, who took keen interest on my project work and guided me all along, till the completion of our project work by providing all the necessary information for developing a good system.

Inspiration

The Data Science Bowl, presented by Booz Allen and Kaggle, is the world’s premier data science for social good competition. The Data Science Bowl brings together data scientists, technologists, domain experts, and organizations to take on the world’s challenges with data and technology. It’s a platform through which people can harness their passion, unleash their curiosity, and amplify their impact to effect change on a global scale