Deep Learning Signal Discrimination for Improved Sensitivity at High Specificity for CMOS Intraoperative Probes.

The challenge in delineating the boundary between cancerous and healthy tissue during cancer resection surgeries can be addressed with the use of intraoperative probes to detect cancer cells labeled with radiotracers to facilitate excision. In this study, deep learning algorithms for background gamma ray signal rejection were explored for an intraoperative probe utilizing CMOS monolithic active pixel sensors optimized toward the detection of internal conversion electrons from [Formula: see text]Tc. Two methods utilizing convolutional neural networks (CNNs) were explored for beta-gamma discrimination: 1) classification of event clusters isolated from the sensor array outputs (SAOs) from the probe and 2) semantic segmentation of event clusters within an acquisition frame of an SAO which provides spatial information on the classification.