PROSTATEx has been superseded by PI-CAI:

Accessing the PROSTATEx Challenge Data Sets

The PROSTATEx Challenge ("SPIE-AAPM-NCI Prostate MR Classification Challenge”) focused on quantitative image analysis methods for the diagnostic classification of clinically significant prostate cancers and was held in conjunction with the 2017 SPIE Medical Imaging Symposium. PROSTATEx ran from November 21, 2016 to January 15, 2017, though a "live" version has also been established at https://prostatex.grand-challenge.org which serves as an ongoing way for researchers to benchmark their performance for this task.

The PROSTATEx-2 Challenge ("SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge" ) ran from May 15, 2017 to June 23, 2017 and was focused on the development of quantitative multi-parametric MRI biomarkers for the determination of Gleason Grade Group in prostate cancer. It was held in conjunction with the 2017 AAPM Annual Meeting (see http://www.aapm.org/GrandChallenge/PROSTATEx-2).

Supplemental data and instructions specific to both challenges are in the Detailed Description section below.

This collection is a retrospective set of prostate MR studies. All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE), and diffusion-weighted (DW) imaging. The images were acquired on two different types of Siemens 3T MR scanners, the MAGNETOM Trio and Skyra. T2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DCE time series was acquired using a 3-D turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. The proton density weighted image was acquired prior to the DCE time series using the same sequence with different echo and repetition times and a different flip angle. Finally, the DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800), and subsequently, the ADC map was calculated by the scanner software. All images were acquired without an endorectal coil.

We created 66 high resolution segmentations for randomly chosen T2-weighted volumes of the SPIE-AAPM-NCI PROSTATEx Challenges (PROSTATEx). The high resolution segmentations were obtained by considering the three scan directions: for each scan direction (axial, sagittal, coronal), the gland was manually delineated by a medical student, followed by a review and corrections of an expert urologist. These three anisotropic segmentations were fused to one isotropic segmentation by means of shape-based interpolation in the following manner: (1) The signed distance transformation of the three segmentations is computed. (2) The anisotropic distance volumes are transformed into an isotropic high-resolution representation with linear interpolation. (3) By averaging the distances, smoothing and thresholding them at zero, we obtained the fused segmentation. The resulting segmentations were manually verified and corrected further by the expert urologist if necessary. Serving as ground truth for training CNNs, these segmentations have the potential to improve the segmentation accuracy of automated algorithms. By considering not only the axial scans but also sagittal and coronal scan directions, we aimed to have higher fidelity of the segmentations especially at the apex and base regions of the prostate. The segmentations to standard DICOM representation were created with dcmqi

This collection is a retrospective set of prostate MR studies. All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE), and diffusion-weighted (DW) imaging. The images were acquired on two different types of Siemens 3T MR scanners, the MAGNETOM Trio and Skyra. T2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DCE time series was acquired using a 3-D turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. The proton density weighted image was acquired prior to the DCE time series using the same sequence with different echo and repetition times and a different flip angle. Finally, the DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in

Segmentations of the prostatic zones and urethra for 200 patients from the publicly avaliable PROSTATEx image dataset [1-3]. The dataset is intended for research purposes in the field of medical imaging and to enable training and evaluation of autmatic segmentation methods.

The prostatic zones and urethra were manually segmented slice by slice on axial T2w MRIs by two experienced radiologists in collaboration with three junior colleagues. All delineations by junior colleagues has been checked, and if necessary, corrected by one of the more experienced radiologists. The two most experienced radiologists have independently delineated duplicate segmentations for 40 patients, for a total of 240 segmentations. This is intended as a test set where automatic methods can compare their performance with the inter-reader variability of the two radiologists.

To ease the use of the dataset, help with structuring the data can be found in the linked GitHub repository.

For more information about the dataset, see the dataset publication:

Holmlund, W., Simkó, A., Söderkvist, K. et al. ProstateZones – Segmentations of the prostatic zones and urethra for the PROSTATEx dataset. Sci Data 11, 1097 (2024). https://doi.org/10.1038/s41597-024-03945-2

[1] Geert Litjens, Oscar Debats, Jelle Barentsz, Nico Karssemeijer, and Henkjan Huisman. "ProstateX Challenge data", The Cancer Imaging Archive (2017). DOI: 10.7937/K9TCIA.2017.MURS5CL

[2] Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33:1083-1092. DOI: 10.1109/TMI.2014.2303821

[3] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

This collection contains prostate’s zonal segmentation for 98 cases randomly selected from the SPIE-AAPM-NCI PROSTATEx Challenges (PROSTATEx). The four-class segmentation encompasses the peripheral zone, transition zone, fibromuscular stroma and the distal prostatic urethra. As underlying images, we used transversal T2w scans. Segmentations were created by a medical student with experience in prostate segmentation and an expert urologist who instructed the student and double-checked the segmentations in the end. The DICOM representation of these segmentations were generated with dcmqi.

Note: Our 245 TCGA cases are ones we identified as having potential for improvement. We plan to upload them in two phases: the first batch of 138 cases, and the second batch of 107 cases in the quality review pipeline, we plan to upload them around early of January, 2025.

  Dataset: A Second Opinion on TCGA PRAD Prostate Dataset Labels with ROI-Level Annotations





  Overview

This dataset provides enhanced Gleason grading annotations for the TCGA PRAD prostate cancer dataset… See the full description on the dataset page: https://huggingface.co/datasets/Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset.

This is the dataset of 100 patients to implement the machine learning algorithm and thereby interpreting results The data set consists of 100 observations and 10 variables (out of which 8 numeric variables and one categorical variable and is ID) which are as follows: Id 1.Radius 2.Texture 3.Perimeter 4.Area 5.Smoothness 6.Compactness 7.diagnosis_result 8.Symmetry 9.Fractal dimension

Dataset

This dataset was created by Falah Gatea

Contents

Multi-parametric magnetic resonance imaging (MRI) dataset for prostate cancer detection. The purpose of this dataset is to help at the development of computer-aided detection and diagnosis (CAD) system of prostate cancer (CaP)

Evaluation results for baseline models.

Model performance using ablated primary dataset (overall soft Dice coefficients).

Links to code and bioRxiv pre-print:

1. Multi-lens Neural Machine (MLNM) Code

2. An AI-assisted Tool For Efficient Prostate Cancer Diagnosis (bioRxiv Pre-print)

Digitized hematoxylin and eosin (H&E)-stained whole-slide-images (WSIs) of 40 prostatectomy and 59 core needle biopsy specimens were collected from 99 prostate cancer patients at Tan Tock Seng Hospital, Singapore. There were 99 WSIs in total such that each specimen had one WSI. H&E-stained slides were scanned at 40× magnification (specimen-level pixel size 0·25μm × 0·25μm) using Aperio AT2 Slide Scanner (Leica Biosystems). Institutional board review from the hospital were obtained for this study, and all the data were de-identified.

Prostate glandular structures in core needle biopsy slides were manually annotated and classified using the ASAP annotation tool (ASAP). A senior pathologist reviewed 10% of the annotations in each slide, ensuring that some reference annotations were provided to the researcher at different regions of the core. It is to be noted that partial glands appearing at the edges of the biopsy cores were not annotated.

Patches of size 512 × 512 pixels were cropped from whole slide images at resolutions 5×, 10×, 20×, and 40× with an annotated gland centered at each patch. This dataset contains these cropped images.

This dataset is used to train two AI models for Gland Segmentation (99 patients) and Gland Classification (46 patients). Tables 1 and 2 illustrate both gland segmentation and gland classification datasets. We have put the two corresponding sub-datasets as two zip files as follows:

1. gland_segmentation_dataset.zip
2. gland_classification_dataset.zip

Table 1: The number of slides and patches in training, validation, and test sets for gland segmentation task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen.

	#Slides
	Train	Valid	Test	Total
Prostatectomy	17	8	15	40
Biopsy	26	13	20	59
Total	43	21	35	99
	#Patches
	Train	Valid	Test	Total
Prostatectomy	7795	3753	7224	18772
Biopsy	5559	4028	5981	15568
Total	13354	7781	13205	34340

Table 2: The number of slides and patches in training, validation, and test sets for gland classification task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. The gland classification datasets are the subsets of the gland segmentation datasets. GS: Gleason Score. B: Benign. M: Malignant.

	#Slides (GS 3+3:3+4:4+3)
	Train	Valid	Test	Total
Biopsy	10:9:1	3:7:0	6:10:0	19:26:1
	#Patches (B:M)
	Train	Valid	Test	Total
Biopsy	1557:2277	1216:1341	1543:2718	4316:6336

NB: Gland classification folder (gland_classification_dataset.zip) may contain extra patches, labels of which could not be identified from H&E slides. They were not used in the machine learning study.

This dataset contributes DICOM-converted annotations to the publicly available National Cancer Institute Imaging Data Commons [1] Prostate-MRI-US-Biopsy collection (https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy). Prostate-MRI-US-Biopsy collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 1017 semi-automatic segmentations of the prostate and 1317 manual segmentations of target lesions in the STL format. Although STL is a common and practical format for 3D printing, it is not interoperable with many visualization and analysis tools commonly used in medical imaging research and does not provide any standard means to communicate metadata, among other limitations.

This dataset contains segmentations of the prostate and target lesions harmonized into DICOM representation. Specifically, we created DICOM Encapsulated 3D Manufacturing Model objects (M3D modality) that includes the original STL content enriched with the DICOM metadata. Furthermore, we created an alternative encoding of the surface segmentations by rasterizing them and saving the result as a DICOM Segmentation object (SEG modality). As a result, the contributed DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer.

Conversion from STL to DICOM M3D modality was performed using PixelMed toolkit (https://www.pixelmed.com/dicomtoolkit.html). Conversion from STL to DICOM SEG was done in 2 steps. We used Slicer (https://www.slicer.org/) to rasterize the surface segmentation to the matrix of the segmented image, which were next converted to DICOM SEGs using dcmqi (https://github.com/QIICR/dcmqi) [5]. Resulting objects were validated using dicom3tools dciodvfy (https://www.dclunie.com/dicom3tools.html). Details describing the conversion process as well as the details on how to access the encapsulated STL content from the DICOM m3D files are provided in this GitHub repository: https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion.

Specific files included in the record are:

1. Prostate-MRI-US-Biopsy-DICOM-Annotations.zip: DICOM M3D and SEG files, organized into the folder hierarchy following this pattern: Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/%SeriesNumber-%Modality-%SeriesDescription.dcm
2. referenced_images_sorted-idc_file_manifest.s5cmd: IDC manifest for downloading the T2W MRI images corresponding to the annotations. To download the files in this manifest, first install s5cmd (https://github.com/peak/s5cmd), and run the following command: s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run referenced_images_sorted-idc_file_manifest.s5cmd. Files will be organized in the Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/ folder hierarchy upon download.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. Cancer Res. 81, 4188–4193 (2021). doi: 10.1158/0008-5472.CAN-21-0950.

[2] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A

[3] Sonn GA, Natarajan S, Margolis DJ, MacAiran M, Lieu P, Huang J, Dorey FJ, Marks LS. Targeted biopsy in the detection of prostate cancer using an office based magnetic resonance ultrasound fusion device. Journal of Urology 189, no. 1 (2013): 86-91. DOI: 10.1016/j.juro.2012.08.095

[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

[5] Herz, C., Fillion-Robin, J.-C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R. & Fedorov, A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Res. 77, e87–e90 (2017). DOI: 10.1158/0008-5472.CAN-17-0336.

This dataset contains 114 t2-weighted MRI images of the prostate with corresponding segmentations.The segmentations include two labels, 1 - Transition Zone, 2 - Peripherial Zone. Most of the images include corresponding PIRADS and PSA values, which are available in the file PSA_PIRADS.csv.

For more information concerning the images, see the following article.

Please cite the following articles, if you are using this dataset:

Gibala, S.; Obuchowicz, R.; Lasek, J.; Schneider, Z.; Piorkowski, A.; Pociask, E.; Nurzynska, K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. J. Clin. Med. 2023, 12, 2836. https://doi.org/10.3390/jcm12082836

Gibała, S.; Obuchowicz, R.; Lasek, J.; Piórkowski, A.; Nurzynska, K. Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol. Appl. Sci. 2023, 13, 9871. https://doi.org/10.3390/app13179871

Multimodal image registration between pre-operative and intra-operative imaging enables the fusion of clinically important information during many surgical and interventional tasks. The registration of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images assists prostate biopsy and focal therapy, arguably having transformed prostate cancer patient care to a less invasive and more localized diagnostic, monitoring and treatment pathway. Though, even with great progress having been made by the community in the past two decades, challenges remain in this application. First, paired MRI and TRUS data from a sizable patient cohort are not routinely stored in clinical practice, and publicly-accessible data is scarce and low-quality. Second, annotating anatomical and pathological landmarks on both images - critical in representing corresponding locations for validation - requires expert domain knowledge and experience from multiple disciplines including urology, radiology and pathology.

In addition to its prevalence-warranted clinical importance, this is also a unique application that saw a wide range of registration algorithms proposed and housed intriguing debates such as rigid-versus-nonrigid and FLE-versus-TRE. Both feature- and intensity-based classical methods and unsupervised or segmentation-driven learning methods have been innovated with some most technically interesting approaches in the field such as biomechanical regularisation and statistical motion modelling.

The mu-Reg challenge aims to provide well-curated, yet real-world clinical data, with more than a hundred paired MR and TRUS images, annotated carefully by researchers and clinicians with more than 15 years of experience working with this application. The outcome of the challenge includes one of the first multimodal imaging data, facilitated with expert annotations for validation, for benchmarking advancement in registration methodology, as well as for future research in managing the most common non-skin cancer in men.

The training and validation data may be used within the research remit of this challenge and in further research-related publications. The training and validation data are not to be used commercially. However, if the desired use is unclear, the organizers ask that those accessing the data refrain from further use or distribution outside of this challenge.

Challenge information is accessible at: https://muregpro.github.io/

Introduction

The Surgical Planning Laboratory (SPL) and the National Center for Image Guided Therapy (NCIGT) are making this dataset available as a resource to aid in the development of algorithms and tools for deformable registration, segmentation and analysis of prostate magnetic resonance imaging (MRI) and ultrasound (US) images.

Description

This dataset contains anonymized images of the human prostate (N=3 patients) collected during two sessions for each patient:

1. MRI examination of the prostate for the purposes of disease staging.
2. US examination of the prostate for the purposes of volumetric examination in preparation to the brachytherapy implant.

These are three-dimensional (multi-slice) scalar images.

Image files are stored using NRRD file format (files with .nrrd extension), see details at http://teem.sourceforge.net/nrrd/format.html. Each image file includes a code for the case number (internal numbering at the research site) and the modality (US or MR).

Image annotations were prepared by Dr. Fedorov (no professional training in radiology) and Dr. Tuncali (10+ in prostate imaging interpretation). Annotations include

1. Manual contouring (segmentation) of the whole prostate gland, performed in 3D Slicer software. These segmentation images are coded in the same fashion as the image files, and saved in NRRD format, with "-label" suffix.
2. Manually placed points (fiducials) corresponding to the location of urethra entry into the prostate at base (coded as UB), verumontanum (VM), urethra entry into the prostate at apex (UA), as well as centroids of cysts and calcifications. UB, UA and VM locations are annotated both in MR and US for all cases, while cysts and calcifications are annotated when applicable. Fiducial points are stored in comma-separated CSV-style format adopted by 3D Slicer software (.fcsv file extension). There is one row per point in these files, encoding the location of the point in RAS coordinate space relative to the image data, and the name of the point.

Viewing the collection

We tested visualization of images, segmentations and fiducials in 3D Slicer software, and thus recommend 3D Slicer as the platform for visualization. 3D Slicer is a free open source platform (see http://slicer.org), with the pre-compiled binaries available for all major operating systems. You can download 3D Slicer at http://download.slicer.org.

Acknowledgments

Preparation of this data collection was made possible thanks to the funding from the National Institutes of Health (NIH) through grants R01 CA111288 and P41 RR019703.

If you use this dataset in a publication, please cite the following manuscript. You can also learn more about this dataset from the publication below.

Fedorov, A., Khallaghi, S., Antonio Sánchez, C., Lasso, A., Fels, S., Tuncali, K., Sugar, E. N., Kapur, T., Zhang, C., Wells, W., Nguyen, P. L., Abolmaesumi, P. & Tempany, C. Open-source image registration for MRI–TRUS fusion-guided prostate interventions. Int J CARS 10, 925–934 (2015). https://pubmed.ncbi.nlm.nih.gov/25847666/

Contact

Andrey Fedorov, fedorov@bwh.harvard.edu

The graph representation of the Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Prostate dataset.

Total RNA Seq: 15 Samples (2 patients (MF1 and MF3)) (HiSeq) and Poly A RNA Seq: 27 Samples (4 patients Normal and Tumour ) (HiSeq)

The diagnosis of prostate cancer using histopathology is reliant on the accurate interpretation of stained or labelled tissue sections. Current standards rely on assessment following Haematoxylin and Eosin (H&E) staining, which is often difficult to interpret and introduces inter-observer variability. Here, we present a digital pathology atlas for prostate cancer tissue, using micrographs of both H&E and our novel set of three biomarkers as an interactive tool, where clinicians and scientists can explore high resolution histopathology from various case studies. The digital pathology prostate cancer atlas when used in conjunction with the biomarkers, will greatly assist pathologists to accurately grade prostate cancer tissue samples. This repository also contains the original image dataset used to generate the case studies for the digital pathology atlas. The data consist of a benign case and cases of ISUP grades from 1 to 5, where initial grading was performed on the hematoxyli..., All images were acquired using a Carl ZEISS AxioScan.Z1 microscope, with a Planapochromat 40x/0.95 objective. Images were orginially stored in the native Carl ZEISS Image format (*.CZI), and then converted to BigTIFF format using Carl ZEISS Zen Blue Software (version 3.8). Lossless compression was also used to minimise file sizes, while retaining image quality. All images can be using freely available imaging software, such as ImageJ or FiJi., , # Reinterpretation of prostate cancer pathology by Appl1, Sortilin and Syndecan-1 biomarkers

https://doi.org/10.5061/dryad.v9s4mw749

We present a digital pathology atlas for prostate cancer tissue, using micrographs of both H&EÂ (hematoxylin and eosin) and our novel set of three biomarkers as an interactive tool, where clinicians and scientists can explore high resolution histopathology from various case studies. The digital pathology prostate cancer atlas when used in conjunction with the biomarkers, will greatly assist pathologists to accurately grade prostate cancer tissue samples. The full set of digital micrographs are also accessible in this repository, along with the grading and patient outcome data.

Description of the data and file structure

The digital pathology atlas for prostate cancer is a fully interactive portable document format (PDF) file, where users can easily explore high resolution histopathology from various case ...

Large set of whole-slide-images (WSI) of prostatectomy specimens with various grades of prostate cancer (PCa). More information can be found in the corresponding paper: https://doi.org/10.1038/s41598-018-37257-4

The WSIs in this dataset can be viewed using the open-source software ASAP or Open Slide.

Due to the large size of the complete dataset, the data has been split up in to multiple archives.

The data from the training set:

• peso_training_masks.zip: Training masks (N=62) that have been used to train the main network of our paper. These masks are generated by a trained U-Net on the corresponding IHC slides.
• peso_training_masks_corrected.zip: A subset of the color deconvolution masks (N=25) on which manual annotations have been made. Within these regions, stain and other artifacts have been removed.
• peso_training_colordeconvolution.zip: Mask files (N=62) containing the P63&CK8/18 channel of the color deconvolution operation. These masks mark all regions that are stained by either P63 or CK8/18 in the IHC version of the slides.
• peso_training_wsi_{1-6}.zip: Zip files containing the whole slide images of the training set (N=62). Each archive contains 10 slides, excluding the last which contains 12. These images are exported at a pixel resolution of 0.48mu/pixels.

The data from the test set:

• peso_testset_regions.zip: Collection of annotation XML files with outlines of the test regions. These can be used to view the test regions in more detail using ASAP.
• peso_testset_png.zip: Export of the test set regions in PNG format (2500x2500 pixels per region).
• peso_testset_png_padded.zip: Export of the test regions in PNG format padded with a 500 pixel wide border (3500x3500 pixels per region). Useful for segmenting pixels at the border of the regions.
• peso_testset_mapping.csv: A csv file mapping files from the test set (numbered 1-160) to regions in the xml files. The csv file also contains the label (benign or cancer) for each region.
• peso_testset_wsi_{1-4}.zip: Zip files containing the whole slide images of the test set (N=40). Each archive contains 10 slides of the test set. These images are exported at a pixel resolution of 0.48mu/pixels.

This study was financed by a grant from the Dutch Cancer Society (KWF), grant number KUN 2015-7970.

If you make use of this dataset please cite both the dataset itself and the corresponding paper: https://doi.org/10.1038/s41598-018-37257-4