Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images
arxiv(2023)
摘要
Wireless Capsule Endoscopy (WCE) is being increasingly used as an alternative
imaging modality for complete and non-invasive screening of the
gastrointestinal tract. Although this is advantageous in reducing unnecessary
hospital admissions, it also demands that a WCE diagnostic protocol be in place
so larger populations can be effectively screened. This calls for training and
education protocols attuned specifically to this modality. Like training in
other modalities such as traditional endoscopy, CT, MRI, etc., a WCE training
protocol would require an atlas comprising of a large corpora of images that
show vivid descriptions of pathologies and abnormalities, ideally observed over
a period of time. Since such comprehensive atlases are presently lacking in
WCE, in this work, we propose a deep learning method for utilizing already
available studies across different institutions for the creation of a realistic
WCE atlas using StyleGAN. We identify clinically relevant attributes in WCE
such that synthetic images can be generated with selected attributes on cue.
Beyond this, we also simulate several disease progression scenarios. The
generated images are evaluated for realism and plausibility through three
subjective online experiments with the participation of eight gastroenterology
experts from three geographical locations and a variety of years of experience.
The results from the experiments indicate that the images are highly realistic
and the disease scenarios plausible. The images comprising the atlas are
available publicly for use in training applications as well as supplementing
real datasets for deep learning.
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