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.
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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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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.
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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.
PETRAW data set was composed of 150 sequences of peg transfer training sessions. The objective of the peg transfer session is to transfer 6 blocks from the left to the right and back. Each block must be extracted from a peg with one hand, transferred to the other hand, and inserted in a peg at the other side of the board. All cases were acquired by a non-medical expert on the LTSI Laboratory from the University of Rennes. The data set was divided into a training data set composed of 90 cases and a test data set composed of 60 cases. A case was composed of kinematic data, a video, semantic segmentation of each frame, and workflow annotation.
3 PAPERS • 6 BENCHMARKS
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
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.
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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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The image set contains 180 high-resolution color microscopic images of human duodenum adenocarcinoma HuTu 80 cell populations obtained in an in vitro scratch assay (for the details of the experimental protocol, we refer to (Liang et al., 2007)). Briefly, cells were seeded in 12-well culture plates ($20 \times 10^3$ cells per well) and grown to form a monolayer with 85\% or more confluency. Then the cell monolayer was scraped in a straight line using a pipette tip ($200 \mu L$). The debris was removed by washing with a growth medium and the medium in wells was replaced. The scratch areas were marked to obtain the same field during the image acquisition. Images of the scratches were captured immediately following the scratch formation, as well as after 24, 48 and 72 h of cultivation.
1 PAPER • 1 BENCHMARK