571 research outputs found
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Uncertainty estimation methods are expected to improve the understanding and
quality of computer-assisted methods used in medical applications (e.g.,
neurosurgical interventions, radiotherapy planning), where automated medical
image segmentation is crucial. In supervised machine learning, a common
practice to generate ground truth label data is to merge observer annotations.
However, as many medical image tasks show a high inter-observer variability
resulting from factors such as image quality, different levels of user
expertise and domain knowledge, little is known as to how inter-observer
variability and commonly used fusion methods affect the estimation of
uncertainty of automated image segmentation. In this paper we analyze the
effect of common image label fusion techniques on uncertainty estimation, and
propose to learn the uncertainty among observers. The results highlight the
negative effect of fusion methods applied in deep learning, to obtain reliable
estimates of segmentation uncertainty. Additionally, we show that the learned
observers' uncertainty can be combined with current standard Monte Carlo
dropout Bayesian neural networks to characterize uncertainty of model's
parameters.Comment: Appears in Medical Image Computing and Computer Assisted
Interventions (MICCAI), 201
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
Artificial Intelligence in Radiation Therapy
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy
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