112 datasets found

The PET-CT dataset contains 3D images from 173 patients who were diagnosed with large B-cell lymphomas and underwent PET-CT examination.

Microscopy Images dataset was from the NeurIPS 2022 Cell Segmentation Challenge [34] that focused on cell segmentation in various microscopy images.

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.

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

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

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.

Medical image segmentation task using cardiac magnetic resonance images from different hospitals.

A dataset for semi-supervised learning of echocardiogram views