165 datasets found

The Brain Tumor Segmentation (BraTS) Challenge 2021 dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings, particularly the...

A benchmark dataset and evaluation methodology for video object segmentation.

The MSD Spleen dataset consists of n = 61 portal-venous phase contrast-enhanced abdominal CT scans, out of which n = 41 (67%) contained annotations for the spleen.

The MSD Liver dataset consists of n = 201 portal-venous phase contrast-enhanced abdominal CT scans, out of which n = 131 (65%) contained annotations for the liver and liver tumors.

The Medical Segmentation Decathlon (MSD) is a collection of 10 benchmark datasets for segmentation spanning different body parts and modalities.

A multi-level ConvLSTM model for the automatic segmentation of left ventricle myocardium in infarcted porcine cine MR images

The SMU-Net dataset is not explicitly mentioned in the paper, but it is used as a benchmark for the proposed method.

The PH2 dataset used for evaluation of the proposed deep learning model for automatic skin lesion segmentation on dermoscopic images.

Geometry Sharing Network (GS-Net) for 3D point cloud classification and segmentation

The BraTS 2020 challenge dataset is a multimodal MRI brain tumor segmentation dataset. It contains 369 subjects with 4 MRI modalities (T2 weighted FLAIR, T1 weighted, T1...

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis,...

The Cell Tracking Challenge is a benchmark for cell tracking and segmentation. It consists of several datasets, including Fluo-N2DH-SIM+, DIC-C2DL-HeLa, PhC-C2DH-U373,...

Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.

Knee OsteoArthritis (KOA) dataset used for early detection of KOA (KL-0 vs KL-2) using Vision Transformer (ViT) model with selective shuffled position embedding and key-patch...

A large DNN, containing 3 stacked modules. The segmentation module is an U-Net, trained beforehand to receive X-rays and output segmentation masks.

A large-scale food image segmentation dataset called FoodSeg103 (and its extension FoodSeg154) for fine-grained food image segmentation research.

The MoNuSeg dataset is published for the Multi-organ Nuclei Segmentation challenge in MICCAI 2018. The training dataset consists of 30 images generated from multiple organs...

The ACDC dataset is a large-scale dataset containing images of urban scenes under different weather conditions.

Liver Tumor Segmentation dataset contains images of liver tumors from CT scans. The dataset is used for automatic segmentation of liver tumors.

Brain Anatomy Segmentation from US images dataset contains images of brain anatomy from ultrasound scans. The dataset is used for automatic segmentation of brain ventricles and...