219 datasets found

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.

Swin-Unet: Unet-like pure transformer for medical image segmentation.

Medical Transformer: Gated axial-attention for medical image segmentation.

Unet++: A nested U-Net architecture for medical image segmentation.

Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled...

Liver and tumor segmentation from Computed Tomography (CT) images is a mandatory task in diagnosing, monitoring, and treating liver diseases.

Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning.