Jiang_2022_J._Inst._17_P06021.pdf (7.39 MB)
Download file

Automatic detection of scintillation light splashes using conventional and deep learning methods

Download (7.39 MB)
journal contribution
posted on 22.06.2022, 15:43 authored by Yangfan Jiang, Sarah BugbySarah Bugby, Georgina CosmaGeorgina Cosma

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 Instrumentation

Volume

17

Issue

06

Publisher

IOP Publishing

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher 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

10/05/2022

Publication date

2022-06-17

Copyright date

2022

eISSN

1748-0221

Language

en

Depositor

Dr Sarah Bugby. Deposit date: 20 June 2022

Article number

P06021