Publications by authors named "Alan Perry"

4 Publications

  • Page 1 of 1

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Sci Rep 2020 07 6;10(1):11073. Epub 2020 Jul 6.

Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V and V of the PTV, V of the rectum, and heterogeneity index.
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http://dx.doi.org/10.1038/s41598-020-68062-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338467PMC
July 2020

Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision.

Phys Med Biol 2019 07 2;64(13):135001. Epub 2019 Jul 2.

These two authors contributed equally. Author to whom correspondence should be addressed.

A deeply supervised attention-enabled boosted convolutional neural network (DAB-CNN) is presented as a superior alternative to current state-of-the-art convolutional neural networks (CNNs) for semantic CT segmentation. Spatial attention gates (AGs) were incorporated into a novel 3D cascaded CNN framework to prioritize relevant anatomy and suppress redundancies within the network. Due to the complexity and size of the network, incremental channel boosting was used to decrease memory usage and facilitate model convergence. Deep supervision was used to encourage semantically meaningful deep features and mitigate local minima traps during training. The accuracy of DAB-CNN is compared to seven architectures: a variation of U-Net (UNet), attention-enabled U-Net (A-UNet), boosted U-Net (B-UNet), deeply-supervised U-Net (D-UNet), U-Net with ResNeXt blocks (ResNeXt), life-long learning segmentation CNN (LL-CNN), and deeply supervised attention-enabled U-Net (DA-UNet). The accuracy of each method was assessed based on Dice score compared to manually delineated contours as the gold standard. One hundred and twenty patients who had definitive prostate radiotherapy were used in this study. Training, validation, and testing followed Kaggle competition rules, with 80 patients used for training, 20 patients used for internal validation, and 20 test patients used to report final accuracies. Comparator p -values indicate that DAB-CNN achieved significantly superior Dice scores than all alternative algorithms for the prostate, rectum, and penile bulb. This study demonstrated that attention-enabled boosted convolutional neural networks (CNNs) using deep supervision are capable of achieving superior prediction accuracy compared to current state-of-the-art automatic segmentation methods.
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http://dx.doi.org/10.1088/1361-6560/ab2818DOI Listing
July 2019

Competency to stand trial evaluations in a multicultural population: Associations between psychiatric, demographic, and legal factors.

Int J Law Psychiatry 2016 Jul-Aug;47:79-85. Epub 2016 Apr 13.

Department of Psychiatry, Mount Sinai-Roosevelt Hospital, 1111Amsterdam Avenue, New York, NY 10025, USA.

Data were examined from an archival sample of Competency to Stand Trial (CST) reports of 200 consecutive New York City pre-trial defendants evaluated over a five-month period. Approximately a fourth of defendants in the present study were immigrants; many required the assistance of interpreters. The examiners conducting the CST evaluation diagnosed approximately half of the defendants with a primary diagnosis of a psychotic disorder and deemed over half not competent. Examiners reached the same conclusion about competency in 96% of cases, about the presence of a psychotic disorder in 91% of cases, and affective disorder in 85% of cases. No significant differences between psychologists and psychiatrists were found for rates of competency/incompetency opinions. Compared to those deemed competent, defendants deemed not competent had significantly higher rates of prior psychiatric hospitalization and diagnosis of psychotic illness at the time of the CST evaluation but lower rates of reported substance abuse.
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http://dx.doi.org/10.1016/j.ijlp.2016.02.039DOI Listing
January 2018

Dry-heat Depyrogenation Ovens for Pharmaceutical Compounding Facilities.

Int J Pharm Compd 2015 May-Jun;19(3):182-92

Sterilization kills microorganisms in compounded preparations, on the implements used to prepare them, and on the vessels that contain them, but depyrogenation incinerates the remaining debris and renders the treated tool, container, or meditation pyrogen free. Depyrogenation is thus an essential step in the preparation of sterile compounds, and the pharmacist who dispenses those formulations is directly responsible for ensuring their safety, potency, and purity. Dry heat provided by a depyrogenation oven or tunnel is the pharmaceutical gold standard for ensuring the elimination of pyrogens. In this report, we describe several depyrogenation ovens that are compliant with Current Good Manufacturing Practice standards and are appropriate for use in aseptic-compounding facilities that meet the guidelines set forth in United States Pharmacopela Chapter <797>.
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February 2016