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Evaluation framework for algorithms segmenting short axis cardiac MRI
The MICCAI 2009 LV Segmentation Challenge (LV-09) dataset contains 45 subjects with expert annotations. -
UMN dataset
The UMN dataset consists of three different crowd scenes, and the dataset has 11 videos from these scenes, with a resolution of 240 × 320. Each video sequence represents a... -
KERMANY dataset
The KERMANY dataset contains 256 OCT scans from DME patients. -
OPTIMA dataset
The OPTIMA dataset contains 30 OCT volumes divided into training and testing sets, each set containing 15 volumes. -
OPTIMA, KERMANY, and UMN datasets
Three public datasets namely OPTIMA, KERMANY, and UMN were employed to evaluate the proposed method. -
BraTS 2021
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,... -
One-pass multi-task convolutional neural networks for efficient brain tumor s...
One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. -
Abdominal CT Images about Pancreatitis (ACIP)
A real CT image database about pancreatitis from hospitals, built to evaluate the performance of the proposed method for pancreatitis recognition. -
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segme...
The V-Net is a deep learning model for medical image segmentation that uses a U-Net architecture. -
3D Stent and Marker Dataset
A dataset of 3D stents and 3D customized markers for training and testing the 3D shape instantiation framework. -
2018 Data Science Bowl challenge
A dataset for microscopy images, used for testing the proposed DoubleU-Net architecture. -
ISIC-2018 challenge
A dataset for dermoscopy images, used for testing the proposed DoubleU-Net architecture. -
Lesion Boundary Segmentation challenge
A dataset for skin lesion segmentation, used for testing the proposed DoubleU-Net architecture. -
Learnable Weight Initialization for Volumetric Medical Image Segmentation
A learnable weight initialization approach for volumetric medical image segmentation -
SAM-VMNet: Deep Neural Networks For Coronary Angiography Vessel Segmentation
Coronary artery disease (CAD) is one of the most prevalent diseases in the cardiovascular field and one of the major contributors to death worldwide. Computed Tomography... -
Synapse CT Abdomen Segmentation Dataset
Medical image segmentation dataset
