输入关键词或点击标签，按论文、数据资源、竞赛截止日期、征稿与课程缩小范围。 标签：Segmentation 范围：数据资源
AMOS is an abdominal multi-organ segmentation benchmark with CT and MRI cases for evaluating versatile medical image segmentation models. It supports abdominal organ segmentation, modality-general segmentation, and benchmarking of robust 3D segmentation methods.
REFUGE is a retinal fundus imaging challenge dataset for glaucoma assessment. It supports glaucoma classification, optic disc and cup segmentation, fovea localization, and fair comparison of ophthalmology AI methods on color fundus photographs.
ACDC is a cardiac MRI dataset for automated cardiac diagnosis and segmentation. It supports left and right ventricular segmentation, myocardium segmentation, cardiac function quantification, and evaluation of robust cardiac image analysis methods.
OASIS provides open-access neuroimaging datasets for studying normal aging, dementia, and brain structure. It is useful for brain MRI segmentation, age prediction, dementia classification, longitudinal modeling, and neuroimaging method benchmarking.
EchoNet-Dynamic is a cardiac ultrasound video dataset with expert annotations for left ventricular function. It is used for echocardiography video understanding, ejection fraction estimation, cardiac segmentation, and clinical video AI research.
The ISIC Archive is a large public dermatology image repository for skin lesion analysis. It is widely used for melanoma classification, lesion segmentation, dermoscopic image retrieval, bias and domain shift analysis, and clinical imaging benchmark development.
BraTS 2024 provides multimodal brain MRI data and expert annotations for brain tumor segmentation and related tumor subregion analysis. It is a major benchmark for glioma segmentation, radiology AI, and robust multimodal MRI segmentation methods.
C4KC-KiTS is a TCIA imaging collection associated with kidney and kidney tumor segmentation benchmarks. It supports kidney segmentation, renal tumor segmentation, surgical planning research, and evaluation of abdominal CT segmentation models.
LIDC-IDRI is a lung CT dataset with thoracic CT scans and expert nodule annotations. It is a classic benchmark for lung nodule detection, segmentation, malignancy characterization, radiomics, and computer-aided diagnosis research.
Legacy multi-task biomedical image segmentation benchmark retained as a reference; newer segmentation benchmarks are listed above it.
Foundation model and toolkit for all-in-one biomedical image parsing across recognition, detection, and segmentation tasks.
Interactive medical image segmentation benchmark and baseline from CVPR 2025, covering multiple modalities, organs, and target structures.