154 datasets found

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...

Multi-modality MRI dataset for brain tumor segmentation

DR-Private is a private dataset of chest X-ray images with annotations.

ChestX-Det is a subset of NIH ChestX-14 with box annotations of 13 categories of diseases.

The EM segmentation dataset has 30 training images with publicly available groundtruth and 30 test images with annotations kept private for the assessment of segmentation accuracy.

Ref-DAVIS17 is an extension of the DAVIS17 dataset, where it enhances the dataset by providing language descriptions for each specific object present in the videos.

Referring video object segmentation (RVOS) aims to accurately segment the target object in the video with the guidance of given language expressions.

Brain tumor segmentation dataset

Brain tumor segmentation dataset

Brain tumor segmentation dataset

Intracranial hemorrhage segmentation using a deep convolutional model

Multi-parametric MRI scans from 2000 patients were used for BraTS2021, 1251 of which were provided with segmentation labels to the participants for developing their algorithms,...