Convolutional neural networks for segmentation and object detection of human semen

We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due to clutter. Our results indicate that training on full images is superior to training on patches when class-skew is properly handled. Full image training including up-sampling during training proves to be beneficial in deep CNNs for pixel wise accuracy and detection performance. Predicted sperm cells are found by using connected components on the CNN predictions. We investigate optimization of a threshold parameter on the size of detected components. Our best network achieves 93.87% precision and 91.89% recall on our test dataset after thresholding outperforming a classical mage analysis approach.Comment: Submitted for Scandinavian Conference on Image Analysis 201

Regional locus coeruleus degeneration is uncoupled from noradrenergic terminal loss in Parkinson’s disease

Previous studies have reported substantial involvement of the noradrenergic system in Parkinson’s disease. Neuromelanin-sensitive MRI sequences and PET tracers have become available to visualize the cell bodies in the locus coeruleus and the density of noradrenergic terminal transporters.Combining these methods, we investigated the relationship of neurodegeneration in these distinct compartments in Parkinson’s disease. We examined 93 subjects (40 healthy controls and 53 Parkinson’s disease patients) with neuromelanin-sensitive turbo spin-echo MRI and calculated locus coeruleus-to-pons signal contrasts. Voxels with the highest intensities were extracted from published locus coeruleus coordinates transformed to individual MRI. To also investigate a potential spatial pattern of locus coeruleus degeneration, we extracted the highest signal intensities from the rostral, middle, and caudal third of the locus coeruleus. Additionally, a study-specific probabilistic map of the locus coeruleus was created and used to extract mean MRI contrast from the entire locus coeruleus and each rostro-caudal subdivision. Locus coeruleus volumes were measured using manual segmentations