225 datasets found

This dataset contains abdominal CT and MRI images from 40 patients.

A dataset used for medical image segmentation tasks

The Medical Segmentation Decathlon (MSD) dataset contains 282 CT scans in portal venous phase, all of which are pathological cases with pancreas and tumor annotation.

The BraTS21 dataset consists of 1251 brain MRI scans of four different weightings (T1, T1-CE, T2, FLAIR).

Medical image segmentation dataset used for pre-training and fine-tuning the SwinFUSE model.

Brain Tumor Segmentation dataset contains images of brain tumors from MRI scans. The dataset is used for automatic segmentation of brain tumors.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a large-scale medical image segmentation dataset.

The BUSI dataset is a collection of ultrasound images depicting normal, benign, and malignant breast cancer cases along with their corresponding segmentation maps.

Medical image segmentation is vital to the area of imaging because it enables professionals to more accurately examine and understand the information offered by different...

The dataset used for training and testing the proposed D-Persona model for multi-rater medical image segmentation.

Polyp(CVC-ClinicDB and ETIS-LaribPolypDB), Skin(ISIC-2018 Skin lesion segmentation challenge dataset)

The dataset used in the paper for medical image segmentation tasks.

The dataset used in the paper for medical image segmentation tasks.

The ClinicDB dataset comprises 100 images for testing.

The MoNuSeg dataset comprises 30 microscopic images from seven organs in the training set, with annotations of 21,623 individual nuclei, and 14 similar images in the test set.

The liver tumor segmentation benchmark (LiTS) dataset is a dataset of liver lesions.

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed.

The CHASE_DB1 dataset is a publicly available dataset for retinal vessel segmentation.

The proposed SA-UNet is evaluated on two publicly available retinal fundus image datasets: DRIVE and CHASE_DB1.

The CAMUS dataset includes 450 annotated patient sub-datasets consisting of TTE apical four- and two-chamber views.