225 datasets found

Endoscopy images dataset was from the MICCAI 2017 EndoVis Challenge [1] that focused on segmenting seven instruments from endoscopy images.

Abdomen MRI dataset was from the MICCAI 2022 AMOS Challenge [25] that also focused on abdominal organ segmentation.

Abdomen CT dataset was from the MICCAI 2022 FLARE Challenge [35] that focused on the segmentation of 13 abdominal organs, including the liver, spleen, pancreas, right kidney,...

The dataset contains images of medical images and corresponding labels.

The BTCV dataset contains abdominal CT images for segmentation tasks.

Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be...

The Abdominal Multi Organ Segmentation 2022 (AMOS2022) challenge dataset contains 500 CT and 100 MRI scans collected from multiple sites, a wide range of imaging conditions, and...

Semantic object segmentation is a fundamental task in medical image analysis and has been widely used in automatic delineation of regions of interest in 3D medical images, such...

The ISBI 2013 challenge dataset is a collection of medical images with corresponding segmentations.

Medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentation models in resource-constrained scenarios like primary...

A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Two large multiclass data sets for prostate MR and abdominal CT

A multi-view multi-stage and multi-window framework for pulmonary artery segmentation from CT scans

The dataset used for prostate segmentation in radiotherapy, cardiac cine-MRI segmentation, inner ear segmentation, and long-axis echocardiographic image segmentation.

Automated medical image segmentation can assist doctors to diagnose faster and more accurate. Deep learning based models for medical image segmentation have made great progress...

The DIAMANT dataset is a medical image segmentation dataset used to evaluate the proposed DIAMANT architecture.

The MICCAI 2019 StructSeg challenge dataset for GTV segmentation of nasopharynx cancer from CT images.

The TotalSegmentator-V2 dataset is a publicly available dataset for 3D medical image segmentation. It contains 1,228 CT scans with annotations for 117 major anatomical...

A novel prompt-driven framework for automatic nucleus instance segmentation in histology images.

Segment Anything Model (SAM) is a prompt-driven foundation model for natural image segmentation, which is trained on the large-scale SA-1B dataset of 1B masks and 11M images.