Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning.

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios.

Optimal Broadband Noise Matching to Inductive Sensors: Application to Magnetic Particle Imaging.

Inductive sensor-based measurement techniques are useful for a wide range of biomedical applications. However, optimizing the noise performance of these sensors is challenging at broadband frequencies, owing to the frequency-dependent reactance of the sensor. In this work, we describe the fundamental limits of noise performance and bandwidth for these sensors in combination with a low-noise amplifier.

Quantitative Magnetic Particle Imaging Monitors the Transplantation, Biodistribution, and Clearance of Stem Cells In Vivo.

Stem cell therapies have enormous potential for treating many debilitating diseases, including heart failure, stroke and traumatic brain injury. For maximal efficacy, these therapies require targeted cell delivery to specific tissues followed by successful cell engraftment. However, targeted delivery remains an open challenge.