Medical Imaging

This dataset is for the paper titled: Segmentation of Cervical Cell Images based on Generative Adversarial Networks. The dataset is used to train and test the Cell-GAN, a generative adversarial network. After training, the Cell-GAN is able to generate a complete single-cell image which has the similar contour to the cell to be segmented.

We make our dataset publicly avaiable. It consists of 50 H&E stained histopathology annotated images at the nuclei level. This dataset is ideal for those who want an exhaustive annotation of H&E breast cancer patient from a Tripple Negative Breast Cancer cohort.

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these type of noise artifacts for removal in experimental photoacoustic data.

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.