TY - GEN
T1 - Crystal Area Segmentation for a Scintillation Detector based on Convolutional Neural Network
AU - Leem, Seowung
AU - Yu, Byeongjae
AU - Cha, Hyemi
AU - Cho, Kyeyoung
AU - Miyaoka, Robert
AU - Kang, Cheolung
AU - Lee, Jongmyoung
AU - Bae, Seungbin
AU - Lee, Hakjae
AU - Lee, Kisung
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. NRF-2019M2D2A1A02059221) and National Institutes of Health under grants CA-74135, CA-86892 and EB0217.
Publisher Copyright:
© 2020 IEEE
PY - 2020
Y1 - 2020
N2 - Crystal area segmentation is one of the critical procedures for decoding the detector module coupled with scintillation crystal. However, the blurring effect makes the decoding procedure challenging. For precise decoding, we propose a crystal area segmentation method based on convolutional neural network (CNN). The method is divided into training stage and evaluation stage. In the training stage, data set was extracted from five flood maps in blocks. These blocks went over preprocessing with bandpass filter (BPF) and thresholding. Then the processed blocks were used to train and test the CNN. In evaluation stage, flood map from 2 positron emission tomography (PET) scanners were tested. The method showed 99.5% and 99.4% of peak detection accuracy for each test samples while existing method achieved 91.1% and 95.4%. The proposed algorithm detected center peaks almost perfectly and improved detectability of boundary peaks. Also, the whole decoding process was done in short amount of time. However, the algorithm proposed in this paper only considered the spatial information of the peaks in flood map. In further studies we will develop improved algorithm with using both spatial and energy information to develop more precise and practical decoding algorithm.
AB - Crystal area segmentation is one of the critical procedures for decoding the detector module coupled with scintillation crystal. However, the blurring effect makes the decoding procedure challenging. For precise decoding, we propose a crystal area segmentation method based on convolutional neural network (CNN). The method is divided into training stage and evaluation stage. In the training stage, data set was extracted from five flood maps in blocks. These blocks went over preprocessing with bandpass filter (BPF) and thresholding. Then the processed blocks were used to train and test the CNN. In evaluation stage, flood map from 2 positron emission tomography (PET) scanners were tested. The method showed 99.5% and 99.4% of peak detection accuracy for each test samples while existing method achieved 91.1% and 95.4%. The proposed algorithm detected center peaks almost perfectly and improved detectability of boundary peaks. Also, the whole decoding process was done in short amount of time. However, the algorithm proposed in this paper only considered the spatial information of the peaks in flood map. In further studies we will develop improved algorithm with using both spatial and energy information to develop more precise and practical decoding algorithm.
KW - Classification
KW - Convolutional neural network
KW - Crystal segmentation
UR - http://www.scopus.com/inward/record.url?scp=85124704859&partnerID=8YFLogxK
U2 - 10.1109/NSS/MIC42677.2020.9507967
DO - 10.1109/NSS/MIC42677.2020.9507967
M3 - Conference contribution
AN - SCOPUS:85124704859
T3 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
BT - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
Y2 - 31 October 2020 through 7 November 2020
ER -