عنوان

DoseGAN:

شماره

LA38f0c0rk

عنوان اصلي

DoseGAN:

نام عام مواد

[Article]

نام نخستين پديدآور

Kearney, Vasant; Chan, Jason W; Wang, Tianqi; Perry, Alan; Descovich, Martina; Morin, Olivier; Yom, Sue S; Solberg, Timothy D

عنوان اصلي به قلم نويسنده ديگر

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

متن يادداشت

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 V100 and V120 of the PTV, V60 of the rectum, and heterogeneity index.

تاريخ نشر

2020

عنوان

UCSF

نام الکترونيکي

نوع ماده

[Article]

کد کاربرگه

275578

سطح دسترسي

a

تكميل شده

Y