Medical Imaging
UTERUS: The uterus dataset (Huang et al. 2021) collected from the treatment device HIFU Pro2008 of Shenzhen ProHuiren Company. The dataset comprises 495 HIFU treatment ultrasound monitoring images of uterus, with 330 images randomly selected for training, 50 images for validating and 115 images for testing. The target region for treatment is the tumor region in the ultrasound image, as marked by professional doctors, and the image size is 448×544 pixels.
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This synthetic dataset or phantom consists of 3 jpg format databases, in the two-dimensional (2-D) domain, which are identified as follows:
DB1: Ground Truth
DB2: Speckle noise with zero mean and 0.005 standard deviation
DB3: Speckle noise with zero mean and 0.05 standard deviation
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This data set contains:
- Training dataset: 271 CT-scans of inner ears used for optimization and training of the model.
- Validation dataset: 70 CT-scans of inner ears used for external validation.
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386 Views
The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.
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Autism spectrum disorder (ASD) is characterized by qualitative impairment in social reciprocity, and by repetitive, restricted, and stereotyped behaviors/interests. Previously considered rare, ASD is now recognized to occur in more than 1% of children. Despite continuing research advances, their pace and clinical impact have not kept up with the urgency to identify ways of determining the diagnosis at earlier ages, selecting optimal treatments, and predicting outcomes. For the most part this is due to the complexity and heterogeneity of ASD.
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1547 Views
Fundus Image Myopia Development (FIMD) dataset contains 70 retinal image pairs, in which, there is obvious myopia development between each pair of images. In addition, each pair of retinal images has a large overlap area, and there is no other retinopathy. In order to perform a reliable quantitative evaluation of registration results, we follow the annotation method of Fundus Image Registration (FIRE) dataset [1] to label control points between the pair of retinal images with the help of experienced ophthalmologists. Each image pair is labeled with
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This dataset contains MRI data acquired approximately 20 minutes before ("Pre-ablation") and 20-40 minutes after ("Post-ablation") MR-guided focused ultrasound thermal ablations in the muscle tissue (quadriceps) in four (n=4) New Zealand white rabbits. MR images include MR thermometry acquired with the proton resonance frenquency method, ADC maps (b=0,400), T2-weighted, and pre- and post-contrast enhanced T1-weighted images. All MRI acquisitions were 3D except for the ADC acquisition, which was 2D multi-slice.
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This dataset is collected from Kaggle ( https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset ). This dataset is a combination of the following three datasets :
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The demand for artificial intelligence (AI) in healthcare is rapidly increasing. However, significant challenges arise from data scarcity and privacy concerns, particularly in medical imaging. While existing generative models have achieved success in image synthesis and image-to-image translation tasks, there remains a gap in the generation of 3D semantic medical images. To address this gap, we introduce Med-DDPM, a diffusion model specifically designed for semantic 3D medical image synthesis, effectively tackling data scarcity and privacy issues.
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2635 Views