Automatic detection of scintillation light splashes using conventional and deep learning methods
Six methods for the automatic detection of scintillation light splashes in a portable gamma camera are compared. Each imaging frame might contain any number of light splashes (including none), and the location and size of each light splash must be identified. For real-time imaging, splashes must be identified and characterised quickly and with minimal processing overhead. The techniques are compared on their ability to accurately determine the number, position, and size of light splashes, and to reconstruct the deposited energy within each splash for a simulated data set with known ground-truths. The speed of each technique and the ease of implementation are also discussed. For accuracy in blob (light splash) localisation, a Laplacian of Gaussian approach was found to provide the most accurate estimation. However, its performance greatly relies on the appropriate tuning of preprocessing parameters prior to image analysis and the number of blobs in each frame. Deep learning approaches (Faster-RCNNs) performed significantly better than traditional algorithms in terms of predicting the size of each light splash, did not require image preprocessing and were also more stable over a range of frame occupancies. Moreover, the paper fine-tuned a VGG16 based Faster-RCNN model with the simulated data set for the scintillation light splash detection, called DeepSplashSpotter (DSS).
Funding
Loughborough University
STFC Cancer Diagnosis Network+ (ST/S005404/1)
History
School
- Science
Department
- Computer Science
- Physics
Published in
Journal of InstrumentationVolume
17Issue
06Publisher
IOP PublishingVersion
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by IOP Publishing under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/Acceptance date
2022-05-10Publication date
2022-06-17Copyright date
2022eISSN
1748-0221Publisher version
Language
- en