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.

Content

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

1. "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).
2. "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

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.

The dataset used in this challenge consists of 165 images derived from 16 H&E stained histological sections of stage T3 or T42 colorectal adenocarcinoma. Each section belongs to a different patient, and sections were processed in the laboratory on different occasions. Thus, the dataset exhibits high inter-subject variability in both stain distribution and tissue architecture. The digitization of these histological sections into whole-slide images (WSIs) was accomplished using a Zeiss MIRAX MIDI Slide Scanner with a pixel resolution of 0.465µm.

This is a set of 1,608,060 image patches of hematoxylin & eosin stained histological samples of various human cancers.

Whole Slide Images of TCGA dataset from 32 solid cancer types were downloaded from GDC legacy database during December 1, 2016 to June 19, 2017. 9,662 diagnostic slides (the filename contains ’DXn’, where n stands for the slide number) from 7,951 patients in SVS format were then processed to annotate.

For each slide, at least three representative tumor regions were selected as polygons by two trained pathologists using a Web browser-based software developed for this purpose. The pathologists selected uniform tumor regions and avoided the regions with noncancerous structures as much as possible. 926 slides were removed due to poor staining, low resolution, out of focus across a slide, no cancerous regions, or incorrect cancer types. Finally 8,736 diagnostic slides from 7175 patients were remained.

Next, 10 patches with 6 magnification levels from 128 x 128 to 256 x 256 μm were randomly cropped with random angle from each annotated region using keras-OpenSlideGenerator (https://github.com/quolc/keras-OpenSlideGenerator). Each patch was selected so as not to include the region outside the annotated region. The selected region was resized to 256 x 256 pixels. Consequently, the number of patches subjected to the analysis ranged from 264,110 to 271,700.

filename: [cancer_type]/[resolution]/[TCGA Barcode]/[region]-[number]-[pixel resolution in original WSI image].jpg

[resolution]

- 0-> 0.5 μm/pixel

- 1-> 0.6 μm/pixel

- 2-> 0.7 μm/pixel

- 3-> 0.8 μm/pixel

- 4-> 0.9 μm/pixel

- 5-> 1.0 μm/pixel

[TCGA Barcode]

TCGA-XX-XXXX represents patient ID.

Please see https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/ for detail.

According to Cognitive Market Research, the global histology embedding system market size will be USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 4.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD XX million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
The automated histology system held the highest histology embedding system market revenue share in 2024.

Market Dynamics of Histology Embedding System Market

Key Drivers for Histology Embedding System Market

Rising chronic diseases to Increase the Demand Globally

The histology embedding system market has experienced growth due to rise in chronic diseases. Histology embedding systems are crucial for preparing tissue samples for microscopic examination, which is essential in diagnosing and researching these conditions. As chronic diseases become more common due to factors like aging populations and lifestyle changes, the demand for accurate and efficient histological analysis grows. This increased demand for diagnostic precision and research into disease mechanisms fuels the adoption of advanced embedding systems, driving market growth.

Continuous innovations in systems to Propel Market Growth

The histology embedding system market has witnessed steady growth, driven by continuous innovations in systems. Advances in technology lead to the development of automated and semi-automated systems that improve throughput and accuracy in tissue processing. Innovations such as improved temperature control, user-friendly interfaces, and integration with digital pathology platforms streamline workflows and reduce manual intervention. Additionally, developments in software and data management facilitate better tracking and documentation. These innovations address evolving research and clinical needs, making modern systems more efficient and reliable, which fuels demand in the histology embedding system market.

Restraint Factor for the Histology Embedding System Market

High costs of equipment to Limit the Sales

The high costs of equipments constrain the growth of histology embedding system market. These advanced systems, essential for precise tissue processing, often involve sophisticated technology and materials, driving up their price. This can make it challenging for healthcare providers, especially in developing regions, to adopt and integrate these systems into their workflows. Additionally, the high initial investment required may deter potential buyers, limiting the market's growth and adoption rate.

Impact of Covid-19 on the Histology Embedding System Market

The covid-19 significantly impacted the market, causing disruptions in the global supply chain and delaying elective surgeries and routine diagnostics, which reduced demand for histology equipment. However, the pandemic also highlighted the importance of pathology labs in diagnosing and managing diseases, leading to increased investments in healthcare infrastructure. This, coupled with the growing focus on cancer diagnostics and research, is expected to drive market recovery and growth as healthcare systems stabilize post-pandemic. Introduction of the Histology Embedding System Market

A Histology Embedding System is a laboratory instrument used to encase biological tissue samples in a solid medium, typically paraffin wax, to preserve and support the tissue's structure for microscopic examination. This process is essential for producing high-quality histological sections for diagnostic and research purposes. The rise in chronic diseases, innovations in histology embedding systems, growing re...

The Report Covers Cytology Market Trends and the Market is Segmented by Type of Examination (Histology and Cytology), Test Type (Microscopy Tests, Molecular Genetics Tests and Flow Cytometry), End-User (Hospitals and Clinics, Academic and Research Institutes. And Other End Users), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The Market Provides the Value (in USD Million) for the Above-Mentioned Segments.

Content

This data set contains 100 histological image patches of 1000 * 1000 px size. The samples were immunostained for CD34 (3,3'-Diaminobenzidine, DAB [brown]) with hematoxylin (blue) counterstain.

Furthermore, the data set contains a table of blood vessel counts in each image by three blinded observers as well as an automatic count with a method based on the following paper:

Kather, Jakob Nikolas et al. "Continuous Representation Of Tumor Microvessel Density And Detection Of Angiogenic Hotspots In Histological Whole-Slide Images". Oncotarget 6.22 (2015): 19163-19176. http://dx.doi.org/10.18632/oncotarget.4383

Image format

All images are RGB, 0.50 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 and liver metastases) 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 Declaration of Helsinki.

Contact

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

# # # 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

Dataset

This dataset was created by RahulKumar

Contents

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

Hyperspectral imaging technology combines the main features of two existing technologies: conventional imaging and spectroscopy. Thus, hyperspectral cameras make it possible to analyze, at the same time and in a non-contact way, the morphological features and chemical composition of the objects captured. The information provided by hyperspectral imaging can be used to detect patterns, cells, or biomarkers to identify diseases. There are different alternatives for processing them and there is a lack of publicly available datasets of medical hyperspectral images. To the best of our knowledge, this is the first open access dataset containing histological hyperspectral images of glioblastoma brain tumors, which can be set as a benchmark for researchers to compare their approaches.

This dataset includes 13 subjects. Each subject has a single histological slide with multiple hyperspectral images captured from each slide where deemed relevant by the pathologists (this number varies for each slide). The database is composed of 469 annotated hyperspectral images from 13 histological slides (482 total images), having a spatial dimension of 800 × 1004 pixels and a spectral dimension of 826 spectral channels. The format of the hyperspectral images is ENVI, the standard format for the storage of hyperspectral images. The ENVI format consists of a flat-binary raster file which may or may not have a file extension, accompanied by an ASCII header file (denoted as *.hdr). The data are stored in band-interleaved-by-line format. In addition, dark and white references were captured to perform a calibration of the raw image, which is a standard procedure in hyperspectral image processing.

The slides were stained with hematoxylin and eosin and captured using a custom hyperspectral microscopic system at 20× magnification. The ground-truth annotation for this dataset is the diagnosis of the slides (tumor _T_ or not tumor _NT_ ) performed by skilled histopathologists after the visual examination of the stained slides, according to the World Health Organization classification of tumors of the nervous system. As far as we are concerned, there are no commercial hyperspectral whole slide scanners. Also, the availability of hyperspectral microscopes is still limited in the market.

The microscope is an Olympus BX-53 (Olympus, Tokyo, Japan). The hyperspectral camera is a Hyperspec® VNIR A-Series from HeadWall Photonics (Fitchburg, MA, USA), which is based on an imaging spectrometer coupled to a charge-coupled device sensor, the Adimec-1000m (Adimec, Eindhoven, Netherlands). This hyperspectral system works in the visual and near-infrared spectral range from 400 to 1000 nm with a spectral resolution of 2.8 nm, sampling 826 spectral channels, and 1004 spatial pixels. The push-broom camera performs a spatial scanning to acquire a hyperspectral cube with a mechanical stage (SCAN, Märzhäuser, Wetzlar, Germany) attached to the microscope, which provides an accurate movement of the slides. The objective lenses are from the LMPLFLN family (Olympus, Tokyo, Japan), optimized for infrared observations.

More information about the dataset can be found in this manuscript.

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

Highlights

• Publicly available dataset with 82 H&E stained images of frozen sections. • Images are acquired on 19 patients with metastatic colon cancer in a liver. • Originally stained and two stain-normalized sets of images included • Pixel wise ground truths provided by seven domain experts.

Dataset

histological picture and definition of histological results

North America histology embedding system market size will be USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.2% from 2024 to 2031. North America has emerged as a prominent participant, and its sales revenue is estimated to reach USD XX Million by 2031. This growth is mainly attributed to the region's growing demand for histopathology services.

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 risks to wildlife and humans from uranium (U) mining to the Grand Canyon watershed are largely unknown. In addition to U, other co-occurring ore constituents contribute to risks to biological receptors depending on their toxicological profiles. This data was collected to characterize the pre-mining concentrations of total arsenic (As), cadmium (Cd), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium (Se), thallium (Tl), U, and zinc (Zn); radiation levels; and histopathologies in biota (vegetation, invertebrates, amphibians, birds, and mammals) at the Canyon Mine.