20 datasets found

A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

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...

Automated medical image segmentation can assist doctors to diagnose faster and more accurate. Deep learning based models for medical image segmentation have made great progress...

Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled...

Liver and tumor segmentation from Computed Tomography (CT) images is a mandatory task in diagnosing, monitoring, and treating liver diseases.

Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning.

Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?

The RIGA+ dataset is a multi-domain joint optic disc (OD) / optic cup (OC) segmentation dataset annotated by six ophthalmologists.

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

The MICCAI BRATS dataset is a fully-annotated dataset for brain tumor segmentation. It contains 220 high-grade subjects and 54 low-grade subjects with four modalities: T1, T1c,...

The DRIVE dataset contains the curvilinear-shaped vessel. This dataset consists of 40 565 × 584 color retinal images, which are split into 20 training images and 20 test images.

The Medical Segmentation Decathlon (MSD) - prostate dataset comprises of 48 (training =32, testing =16) multimodal (T2, ADC) 3D MRI samples.

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,...

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

The V-Net is a deep learning model for medical image segmentation that uses a U-Net architecture.

A learnable weight initialization approach for volumetric medical image 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...

Medical image segmentation dataset

Medical image segmentation dataset

The dataset used in this paper for medical image segmentation, including images from the ISBI-2012 dataset.