Medical Imaging

This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.

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Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the design and deployment of deep learning-based surgical applications such as surgical instrument segmentation.

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Synaptic vesicle glycoprotein 2A (SV2A) is the most widely distributed transmembrane glycoprotein present on secretory vesicles in the pre-synaptic terminal of neurons throughout the central nervous system (Bajjalieh et al., 1994).  SV2A can be used as a marker to visualize pre-synaptic density distribution in vivo using positron emission tomography (PET) imaging thanks to the SV2A radioligands available, including [11C]UCB-J (Nabulsi et al., 2016). Given the brain-wide distribution of SV2A, regional analysis of SV2A PET data may be limiting the amount of information that can be obtained.

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The dataset  includes annotated Computed Tomography (CT)  scanned images. The labels consist of three types:

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This dataset is a private foot pressure image dataset, including 317 images of high arches, 217 images of flat feet, and 362 images of normal feet.

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Relaxation mechanism of magnetic particles is crucial in differentiating particles and estimating temperature and viscosity for diagnosis and treatment. The magnetization recovery process in field flat phase of pulsed excitation generates decay signals that can be fitted by a bi-exponential model. The relaxation time spectrum can be generated by using inverse Laplace transform.

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Hydrogel scaffolds have attracted attention to develop cellular therapy and tissue engineering platforms for regenerative medicine applications. Among factors, local mechanical properties of scaffolds drive the functionalities of cell niche. Dynamic mechanical analysis (DMA), the standard method to characterize mechanical properties of hydrogels, restricts development in tissue engineering because the measurement provides a single elasticity value for the sample, requires direct contact, and represents a destructive evaluation preventing longitudinal studies on the same sample.

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The data is divided into a training set of 999 images and a test set of 335 images. The size of each 2D ultrasound image is 800 by 540 pixels with a pixel size ranging from 0.052 to 0.326 mm. The pixel size for each image can be found in the csv files: ‘training_set_pixel_size_and_HC.csv’ and ‘test_set_pixel_size.csv’. The training set also includes an image with the manual annotation of the head circumference for each HC, which was made by a trained sonographer.

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This dataset includes four sub-datasets: Drishti-GS, RIM-ONE-r3, ORIGA and REFUGE. Each image is cropped around the optic disc area for joint optic disc and cup segmentation. The size of all images is 512×512. The manual pixel-wise annotation is stored as a PNG image with the same size as the corresponding fundus image with the following labels:

128: Optic Disc (Grey color)

0: Optic Cup (Black color)

255: Background (White color)

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