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Meeting ReportPhysics, Instrumentation & Data Sciences

A fully automated deep-learning based method for lesion segmentation in 18F-DCFPyL PSMA PET images of patients with prostate cancer

Kevin Leung, Saeed Ashrafinia, Mohammad Salehi Sadaghiani, Pejman Dalaie, Rima Tulbah, Yafu Yin, Ryan VanDenBerg, Jeffrey Leal, Michael Gorin, Yong Du, Martin Pomper, Steven Rowe and Arman Rahmim
Journal of Nuclear Medicine May 2019, 60 (supplement 1) 399;
Kevin Leung
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
2Biomedical Engineering Johns Hopkins University Baltimore MD United States
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Saeed Ashrafinia
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
3Electrical and Computer Engineering Johns Hopkins University Baltimore MD United States
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Mohammad Salehi Sadaghiani
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Pejman Dalaie
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Rima Tulbah
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Yafu Yin
5The First Hospital of China Medical University Shenyang China
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Ryan VanDenBerg
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Jeffrey Leal
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Michael Gorin
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Yong Du
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Martin Pomper
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
2Biomedical Engineering Johns Hopkins University Baltimore MD United States
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Steven Rowe
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
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Arman Rahmim
4The Russell H. Morgan Department of Radiology Johns Hopkins University Baltimore MD United States
6Radiology and Physics University of British Columbia Vancouver BC Canada
1Integrative Oncology BC Cancer Research Center Vancouver BC Canada
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Abstract

399

Objectives: Reliable segmentation of prostate cancer (PCa) lesions from 18F-DCFPyL prostate-specific membrane antigen (PSMA) PET images is an important need towards discovery and validation of imaging biomarkers for the diagnosis and prognosis of PCa [1,2]. Segmentation of PET images is challenging due to the relatively low spatial resolution and high noise levels [2]. Lesion delineation is typically performed manually, but manual segmentation often suffers from inter- and intra-operator variability [2]. In this work, we aimed to develop a fully automated deep-learning based method for lesion delineation in 18F-DCFPyL PET images. Such fully automated segmentation methods could be utilized to further develop a prognostic tool for PCa and to assist in individualized treatment planning and monitoring.

Methods: 18F-DCFPyL PSMA PET images of 207 patients with PCa were manually segmented by four nuclear medicine physicians. The dataset contained a total of 1,224 PCa lesions where each patient had approximately 6 lesions on average. A deep convolutional neural network (CNN) was developed to delineate those PCa lesions. The 207 patient images were randomly partitioned into a training and test set containing 145 and 62 patients, respectively. The hyperparameters of the network were optimized via a 10-fold cross-validation on the training set. The network was trained with a cross-entropy loss function and a first-order stochastic gradient-based optimization algorithm [3]. The proposed method was then evaluated on the test set. Segmentation accuracy was evaluated on the basis of standard evaluation metrics, including Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), true positive fraction (TPF) and true negative fraction (TNF) [1]. DSC and JSC are measures of overlap where higher values indicate more accurate segmentation. The proposed method was further evaluated on the quantification of PCa lesions based on lesion volume measured in cubic centimeters (cc) and SUVmean. The mean absolute error (MAE) between the lesion volume (MAEvol) and SUVmean (MAESUVmean) values derived from the predicted lesion delineation and the manual segmentation ground truth were quantified. Lower values of MAEvol and MAESUVmean indicate more accurate PCa lesion quantification. The proposed method was also compared to commonly used semi-automated thresholding-based techniques that used thresholds of 30%, 40%, and 50% SUVmax, respectively.

Results: The proposed fully automated deep-learning method yielded a DSC, JSC, MAEvol and MAESUVmean of 0.71 (95% confidence interval (CI): 0.69, 0.72), 0.60 (95% CI: 0.58, 0.61), 0.28 (95% CI: 0.26, 0.30) and 2.23 (95% CI: 2.01, 2.46), respectively, on the test set. The best performing semi-automated thresholding-based technique (30% SUVmax) yielded a DSC, JSC, MAEvol and MAESUVmean of 0.66 (95% CI: 0.65, 0.68), 0.53 (95% CI: 0.52, 0.54), 0.58 (95% CI: 0.52, 0.64) and 3.73 (95% CI: 3.44, 4.02), respectively. Overall, the proposed method significantly (paired sample t-test p-value<0.05) outperformed the semi-automated thresholding-based methods and yielded more accurate segmentation and lesion quantification.

Conclusions: A deep-learning based segmentation method as applied to PSMA PET images was developed and showed significant promise towards automated delineation and quantification of PCa lesions.

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Journal of Nuclear Medicine
Vol. 60, Issue supplement 1
May 1, 2019
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A fully automated deep-learning based method for lesion segmentation in 18F-DCFPyL PSMA PET images of patients with prostate cancer
Kevin Leung, Saeed Ashrafinia, Mohammad Salehi Sadaghiani, Pejman Dalaie, Rima Tulbah, Yafu Yin, Ryan VanDenBerg, Jeffrey Leal, Michael Gorin, Yong Du, Martin Pomper, Steven Rowe, Arman Rahmim
Journal of Nuclear Medicine May 2019, 60 (supplement 1) 399;

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A fully automated deep-learning based method for lesion segmentation in 18F-DCFPyL PSMA PET images of patients with prostate cancer
Kevin Leung, Saeed Ashrafinia, Mohammad Salehi Sadaghiani, Pejman Dalaie, Rima Tulbah, Yafu Yin, Ryan VanDenBerg, Jeffrey Leal, Michael Gorin, Yong Du, Martin Pomper, Steven Rowe, Arman Rahmim
Journal of Nuclear Medicine May 2019, 60 (supplement 1) 399;
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