HAM10000 is a dataset of 10000 training images for detecting pigmented skin lesions. The authors collected dermatoscopic images from different populations, acquired and stored by different modalities.
167 PAPERS • 3 BENCHMARKS
Kvasir-SEG is an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist.
147 PAPERS • 3 BENCHMARKS
The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided..
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The Medical Segmentation Decathlon is a collection of medical image segmentation datasets. It contains a total of 2,633 three-dimensional images collected across multiple anatomies of interest, multiple modalities and multiple sources. Specifically, it contains data for the following body organs or parts: Brain, Heart, Liver, Hippocampus, Prostate, Lung, Pancreas, Hepatic Vessel, Spleen and Colon.
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The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge. Magnetic Resonance (MR) images (T2-weighted) of 50 patients with various diseases were acquired at different locations with several MRI vendors and scanning protocols.
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The BraTS 2015 dataset is a dataset for brain tumor image segmentation. It consists of 220 high grade gliomas (HGG) and 54 low grade gliomas (LGG) MRIs. The four MRI modalities are T1, T1c, T2, and T2FLAIR. Segmented “ground truth” is provide about four intra-tumoral classes, viz. edema, enhancing tumor, non-enhancing tumor, and necrosis.
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The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs, each belonging to an individual patient, and scanned with an Omnyx VL120 scanner within the department of pathology at University Hospitals Coventry and Warwickshire, UK.
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PanNuke is a semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask.
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The ISIC 2018 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. This Task 1 dataset is the challenge on lesion segmentation. It includes 2594 images.
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SegTHOR (Segmentation of THoracic Organs at Risk) is a dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiotherapy. In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist.
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Introduced by Da et al. in DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System
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Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level.
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WORD is a dataset for organ semantic segmentation that contains 150 abdominal CT volumes (30,495 slices) and each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotation, which may be the largest dataset with whole abdominal organs annotation.
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**CrossMoDA is a large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging.
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Consists of annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists.
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BRATS 2016 is a brain tumor segmentation dataset. It shares the same training set as BRATS 2015, which consists of 220 HHG and 54 LGG. Its testing dataset consists of 191 cases with unknown grades. Image Source: https://sites.google.com/site/braintumorsegmentation/home/brats_2016
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REFUGE Challenge provides a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
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We release expert-made scribble annotations for the medical ACDC dataset 1. The released data must be considered as extending the original ACDC dataset. The ACDC dataset contains cardiac MRI images, paired with hand-made segmentation masks. It is possible to use the segmentation masks provided in the ACDC dataset to evaluate the performance of methods trained using only scribble supervision.
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The SD-198 dataset contains 198 different diseases from different types of eczema, acne and various cancerous conditions. There are 6,584 images in total. A subset include the classes with more than 20 image samples, namely SD-128."
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BRATS 2014 is a brain tumor segmentation dataset.
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Cata7 is the first cataract surgical instrument dataset for semantic segmentation. The dataset consists of seven videos while each video records a complete cataract surgery. All videos are from Beijing Tongren Hospital. Each video is split into a sequence of images, where resolution is 1920×1080 pixels. To reduce redundancy, the videos are downsampled from 30 fps to 1 fps. Also, images without surgical instruments are manually removed. Each image is labeled with precise edges and types of surgical instruments. This dataset contains 2,500 images, which are divided into training and test sets. The training set consists of five video sequences and test set consists of two video sequence.
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The Kvasir-SEG dataset includes 196 polyps smaller than 10 mm classified as Paris class 1 sessile or Paris class IIa. We have selected it with the help of expert gastroenterologists. We have released this dataset separately as a subset of Kvasir-SEG. We call this subset Kvasir-Sessile.
The dataset contains a Video capsule endoscopy dataset for polyp segmentation.
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The “Medico automatic polyp segmentation challenge” aims to develop computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps (for example, irregular polyp, smaller or flat polyps) with high efficiency and accuracy. The main goal of the challenge is to benchmark semantic segmentation algorithms on a publicly available dataset, emphasizing robustness, speed, and generalization.
Colorectal Adenoma contains 177 whole slide images (156 contain adenoma) gathered and labelled by pathologists from the Department of Pathology, The Chinese PLA General Hospital.
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A challenge that consists of three tasks, each targeting a different requirement for in-clinic use. The first task involves classifying images from the GI tract into 23 distinct classes. The second task focuses on efficiant classification measured by the amount of time spent processing each image. The last task relates to automatcially segmenting polyps.
2 PAPERS • 1 BENCHMARK
An experimental and synthetic (simulated) OA raw signals and reconstructed image domain datasets rendered with different experimental parameters and tomographic acquisition geometries.
The PAX-Ray++ dataset uses pseudo-labeled thorax CTs to enable the segmentation of anatomy in Chest X-Rays. By projecting the CTs to a 2D plane, we gather fine-grained annotated imaages resembling radiographs. It contains 7,377 frontal and lateral view images each with 157 anatomy classes and over 2 million annotated instances.
CheXlocalize is a radiologist-annotated segmentation dataset on chest X-rays. The dataset consists of two types of radiologist annotations for the localization of 10 pathologies: pixel-level segmentations and most-representative points. Annotations were drawn on images from the CheXpert validation and test sets. The dataset also consists of two separate sets of radiologist annotations: (1) ground-truth pixel-level segmentations on the validation and test sets, drawn by two board-certified radiologists, and (2) benchmark pixel-level segmentations and most-representative points on the test set, drawn by a separate group of three board-certified radiologists.
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EBHI-Seg is a dataset containing 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer.
MTNeuro is a multi-task neuroimaging benchmark built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions.
Projection of RibFrac CT dataset to a 2D plane to imitate X-Ray data for a total of 880 images with multi-label segmentation masks. The dataset contains fine-grained 92 individual labels of anatomical structures, which, when including super-classes, lead to a total of 166 labels in both lateral and frontal view.
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