Our comprehensive evaluation on a publicly available MRI cardiac segmentation dataset, comparing against various SSL frameworks with different UNet-based segmentation networks, highlights the superior performance of Semi-Mamba-UNet. The source code has been made publicly accessible. 中文摘要: 医学图像分割...
突触多器官分割数据集Synapse multi-organ segmentation dataset(Synapse):该数据集包括30例3779个轴向腹部临床CT图像,将18个样本分为训练集,12个样本分为测试集。以平均Dice-Similarity coefficient (DSC)和平均Hausdorff Distance (HD)作为评价指标,对8个腹部器官(主动脉、胆囊、脾脏、左肾、右肾、肝脏、胰腺、脾、...
Table 2. Summary of the multi-modal medical image segmentation datasets. DatasetTrainValidationTestSegmentation TaskModalityImage Size Brats2012 35 N/A 15 Brain tumor T1, T1C, T2, Flair 160×216×176176×176×216 Brats2013 35 N/A 25 Brain tumor T1, T1C, T2, Flair 160×216×176 176×176...
To address this challenge, we curated a diverse and large-scale medical image segmentation dataset with 1,570,263 medical image-mask pairs, covering 10 imaging modalities, over 30 cancer types, and a multitude of imaging protocols (Fig. 1 and Supplementary Tables 1–4). This large-scale ...
2.2 Medical Image Data Medical image data is usually represented as a stack of individual images. A modality is a specific image acquisition technique, such as CT or MRI. Each image of a volume dataset represents a thin slice of the scanned body part and is composed of individual pixels (pi...
[ICLR 2024] FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling - Harvard-Ophthalmology-AI-Lab/FairSeg
在gpu上的医学图像分割-一个综合的综述 Medical image segmentationonGPUs–Acomprehensive review,解剖结构的分割,从计算机断层扫描(CT)、磁共振成像(MRI)和超声波,是诊断、规划和指导等医疗应用的关键启用技术。更有效的实现是必要的,因为大多数分割方法的计算成
[BreastScreening] UTA4: Breast Cancer Medical Imaging DICOM Files Dataset & Resources (MG, US and MRI)https://github.com/MIMBCD-UI/dataset-uta4-dicom [MIMBCD-UI] UTA7: Breast Cancer Medical Imaging DICOM Files Dataset & Resources (MG, US and MRI)https://github.com/MIMBCD-UI/dataset-...
3. Challenges 总而言之,上述tricks的实验表明它们的组合可以解决以下四大挑战: Small dataset learning:数据增强、patching、OverSam、ReSam Class imbalance learning:Patching、OverSam、ReSam和IntesNorm Multi-modality learning:加载预训练权重、TTA Domain adaptation:加载预训练权重、TTA ...
( Image credit: IVD-Net )Benchmarks Add a Result These leaderboards are used to track progress in Medical Image Segmentation TrendDatasetBest ModelPaperCodeCompare Kvasir-SEG DUCK-Net See all CVC-ClinicDB DUCK-Net See all ETIS-LARIBPOLYPDB RAPUNet See all ...