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

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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 labelled with radiotracers to facilitate excision. In this study,
deep learning algorithms for background gamma ray signal
rejection were explored for an intraoperative probe utilising
CMOS monolithic active pixel sensors optimised towards the
detection of internal conversion electrons from 99mTc. Two
methods utilising 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. The feasibility of the methods in this study
was explored with several radionuclides including 14C, 57Co and
99mTc. Overall, the classification deep network is able to achieve an
improved area under the curve (AUC) of the receiver operating
characteristic (ROC), giving 0.93 for 14C beta and 99mTc gamma
clusters, compared to 0.88 for a more conventional feature-based
discriminator. Further optimisation of the lower left region of the
ROC by using a customised AUC loss function during training led
to an improvement of 31% in sensitivity at low false positive rates
compared to the conventional method. The segmentation deep
network is able to achieve a mean dice score of 0.93. Through the
direct comparison of all methods, the classification method was
found to have a better performance in terms of the AUC.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalTransactions on Radiation and Plasma Medical Sciences
Publication statusPublished - 19 Jul 2021


  • Image analysis
  • data correction techniques
  • signal processing
  • device optimisation
  • intraoperative probe
  • CMOS
  • cancer surgeries
  • Deep learning
  • Convolutional Neural Networks
  • Classification


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