The potential for clinical application of automatic quantification of olfactory bulb volume in MRI scans using convolutional neural networks.

The olfactory bulbs (OBs) play a key role in olfactory processing; their volume is important for diagnosis, prognosis and treatment of patients with olfactory loss. Until now, measurements of OB volumes have been limited to quantification of manually segmented OBs, which is a cumbersome task and makes evaluation of OB volumes in large scale clinical studies infeasible. Hence, the aim of this study was to evaluate the potential of our previously developed automatic OB segmentation method for application in clinical practice and to relate the results to clinical outcome measures.

Knowledge distillation with ensembles of convolutional neural networks for medical image segmentation.

Ensembles of convolutional neural networks (CNNs) often outperform a single CNN in medical image segmentation tasks, but inference is computationally more expensive and makes ensembles unattractive for some applications. We compared the performance of differently constructed ensembles with the performance of CNNs derived from these ensembles using knowledge distillation, a technique for reducing the footprint of large models such as ensembles. We investigated two different types of ensembles, namely, diverse ensembles of networks with three different architectures and two different loss-functions, and uniform ensembles of networks with the same architecture but initialized with different random seeds.

Generative models for reproducible coronary calcium scoring.

Coronary artery calcium (CAC) score, i.e., the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events.

Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images.

In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously.