Medical Imaging

These datasets were used to produce the results of the following TMI paper: "3D Quantification of Filopodia in Motile Cancer Cells", Castilla C., et al. (2019). IEEE Transactions on Medical Imaging 38(3):862,872.

 

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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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The investigation of the performance of different Positron Emission Tomography (PET) reconstruction and motion compensation methods requires an accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well- controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes and physiological variations. Alternatively, computational phantoms can be used to generate large datasets for different disease states, providing a ground truth.

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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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A database of lips traces
Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.

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The class of registration methods proposed in the framework of Stokes Large Deformation
Diffeomorphic Metric Mapping is a particularly interesting family of physically
meaningful diffeomorphic registration methods.
Stokes-LDDMM methods are formulated as a conditioned variational problem,
where the different physical models are imposed using the associated partial differential equations
as hard constraints.
The most significant limitation of Stokes-LDDMM framework is its huge computational complexity.

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