DOI: 10.5061/dryad.0cfxpnw5q
title: Hematoxylin-and-eosin-stained bladder urothelial cell carcinoma versus inflammation digital histopathology image dataset

description: Digital pathology requires a large number of well-annotated image datasets
 to benefit from deep learning algorithms. Unfortunately, most available
 datasets are annotated at the slide level; which is not as useful as
 patch-level or pixel-level annotations. Additionally, urinary bladder
 cancer is underrepresented in digital pathology deep learning studies.
 Here, we present an annotated dataset of patch-level images obtained from
 90 hematoxylin-and-eosin-stained histopathology slides of urinary bladder
 lesions. Non-overlapping photographs of all available tissue areas on each
 slide were systematically obtained and manually classified by the
 pathologist in our team as inflammation (5,948 images), urothelial cell
 carcinoma (UCC) (5,811 images), or invalid (3,132 images).
description: The dataset source was 90 formalin-fixed paraffin-embedded
 hematoxylin-and-eosin-stained histopathology slides with 4-μm-thick
 sections of urinary bladder lesions that were either cystitis (43 slides)
 or UCC (47 slides). Slides were obtained from 74 specimens from the
 Departments of Pathology of both the Faculty of Medicine and the Cancer
 Institute in our university. The UCC slides were of different pathological
 stages: pTa (five slides), pT1 (nine slides), pT2 (28 slides), and pT3a
 (five slides). Slides were photographed using an Olympus® E-330 digital
 camera mounted on an Olympus® CX31 light microscope by an Olympus®
 E330-ADU1.2X adapter. Magnification of the microscope was set to 20x.
 Certain camera settings were adjusted before photographing. The shutter
 speed, aperture value, International Organization for Standardization
 (ISO) sensitivity to light, and white balance were set automatically.
 Exposure compensation value, which controls the brightness, was set to
 +1.0. Images were set to have a resolution of 3,136x2,352 pixels, a Joint
 Photographic Experts Group (JPEG) format, and a 1:2.7 compression rate.
 Non-overlapping photographs of all available tissue areas on each slide
 were systematically obtained. This resulted in a total of 14,891
 images. Regardless of the slide-level diagnoses, the
 pathologist in our team manually classified all of the obtained images
 into three categories: inflammation, UCC, and invalid. An image-level
 (also known as patch-level) diagnosis of inflammation was based on the
 presence of inflammatory cell infiltrate in the form of lymphocytes,
 plasma cells, eosinophils, and/or polymorphs, in the absence of any
 malignant cells. An image-level diagnosis of UCC was based on the presence
 of malignant urothelial cells showing features of anaplasia in the form of
 pleomorphism, hyperchromatism, increased nuclear-cytoplasmic ratio, and
 increased mitotic figures. These malignant cells may be arranged in
 papillae, sheets, or groups. They may also be present as single cells. An
 image was considered invalid when it contained no sufficient criteria to
 be included in one of the other two categories, even if it only contained
 normal urinary bladder tissue. Also, an image was considered invalid if it
 contained tissues that were processed too badly to be diagnosed. The
 pathologist's classification resulted in a total of 5,948
 inflammation images, 5,811 UCC images, and 3,132 invalid images. Invalid
 images were not excluded.
description: # Hematoxylin-and-eosin-stained bladder urothelial cell carcinoma versus
 inflammation digital histopathology image dataset KEY WORDS artificial
 intelligence; cystitis; deep learning; histopathology; urothelial cell
 carcinoma BACKGROUND Machine learning, a major branch of artificial
 intelligence, comprises algorithms that can make predictions after being
 trained on prior examples. Deep learning, a subset of machine learning,
 consists of a more recent and more sophisticated category of these
 algorithms. Deep learning includes, but is not limited to, convolutional
 neural networks, which are capable of directly learning from image
 datasets (1,2). This opened the door for a myriad of applications in
 medical image analysis (3). In digital pathology, these applications
 encompass low-level tasks such as nuclei segmentation, mitosis detection,
 and gland segmentation; standard applications such as tumor detection,
 subtyping, grading, and staging; and advanced inferences that cannot be
 reliably done by human experts such as prediction of survival, recurrence,
 treatment response, and mutations (4-6). However, one of the key
 challenges that are still limiting its wide-scale clinical adoption is the
 scarcity of high-quality manually annotated reference datasets (7-9). Even
 worse, most of the available whole-slide images are labeled at the case
 level, as opposed to the much more useful patch-level or pixel-level
 labeling (8,9). An abundance of these hand-annotated images is typically
 needed, not only for developing successful deep learning models, but also
 for externally validating them. Unfortunately, building such large
 datasets is greatly laborious and time-consuming (7-10). Although urinary
 bladder cancer ranks tenth in worldwide cancer incidence (11), it is
 underrepresented in digital pathology deep learning studies (4-6,8,12,13).
 Moreover, in a recent study comparing 19 cancer types, bladder cancer was
 the second easiest-to-classify tissue, the first being breast cancer (14).
 This result suggests that these tumors are canonical for artificial
 intelligence research. AIM To provide a dataset that comprises no less
 than 10,000 high-resolution digital histopathology images of
 non-overlapping regions of hematoxylin-and-eosin-stained urinary bladder
 specimens. The images show either urothelial cell carcinoma (UCC) or
 inflammation. The two classes are reasonably balanced regarding the count
 of images. The labeled dataset is available online for artificial
 intelligence research purposes. METHODS OF BUILDING THE DATASET The
 dataset source was 90 formalin-fixed paraffin-embedded
 hematoxylin-and-eosin-stained histopathology slides with
 4-micrometer-thick sections of urinary bladder lesions that were either
 cystitis (43 slides) or UCC (47 slides). Slides were obtained from 74
 specimens from the Departments of Pathology of both of the Faculty of
 Medicine and the Cancer Institute in our university. The UCC slides were
 of different pathological stages: pTa (five slides), pT1 (nine slides),
 pT2 (28 slides), and pT3a (five slides). Slides were photographed using an
 Olympus(R) E-330 digital camera mounted on an Olympus(R) CX31 light
 microscope by an Olympus(R) E330-ADU1.2X adapter. Magnification of the
 microscope was set to 20x. Certain camera settings were adjusted before
 photographing. The shutter speed, aperture value, International
 Organization for Standardization (ISO) sensitivity to light, and white
 balance were set automatically. Exposure compensation value, which
 controls the brightness, was set to +1.0. Images were set to have a
 resolution of 3,136x2,352 pixels, a Joint Photographic Experts Group
 (JPEG) format, and a 1:2.7 compression rate. Non-overlapping photographs
 of all available tissue areas on each slide were systematically obtained.
 This resulted in a total of 14,891 images. Regardless of the slide-level
 diagnoses, the pathologist in our team manually classified all of the
 obtained images into three categories: inflammation, UCC, and invalid. An
 image-level (also known as patch-level) diagnosis of inflammation was
 based on the presence of inflammatory cell infiltrate in the form of
 lymphocytes, plasma cells, eosinophils, and/or polymorphs, in the absence
 of any malignant cells. An image-level diagnosis of UCC was based on the
 presence of malignant urothelial cells showing features of anaplasia in
 the form of pleomorphism, hyperchromatism, increased nuclear-cytoplasmic
 ratio, and increased mitotic figures. These malignant cells may be
 arranged in papillae, sheets, or groups. They may also be present as
 single cells. An image was considered invalid when it contained no
 sufficient criteria to be included in one of the other two categories,
 even if it only contained normal urinary bladder tissue. Also, an image
 was considered invalid if it contained tissues that were processed too
 badly to be diagnosed. The pathologist's classification resulted in a
 total of 5,948 inflammation images, 5,811 UCC images, and 3,132 invalid
 images. Invalid images were not excluded. HOW THE DATASET IS ORGANIZED The
 images that came from the same slide and belong to the same category were
 archived together in a single ZIP file. The ZIP file name is composed of
 the slide number and the category of the images archived within. A certain
 slide did not necessarily provide all of the three categories; it might
 have provided only two or even one of them. The whole dataset was packed
 in a total of 210 ZIP files, the total size of which is a little more than
 68 gigabytes. MANIFEST OF THE DATASET FILES 210 ZIP files containing the
 dataset images. 1 CSV file listing the following data for each slide:
 Diagnosis: either UCC or cystitis. Pathological stage: only for UCC
 slides. Source specimen: more than one slide may come from the same
 specimen. Count of images that were obtained from the slide and belonging
 to each of the three classification categories. 1 TXT file listing the
 SHA256 checksums of the ZIP files. 1 TXT file describing the dataset (the
 present read-me file). ETHICS APPROVAL Approval of the Institutional
 Review Board to publish the dataset was obtained under the number
 17300658. FUNDING STATEMENT The authors received no specific funding for
 this work. CONFLICT OF INTEREST STATEMENT The authors have no competing
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