Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images

by   Anuja Vats, et al.

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.


page 1

page 4

page 5

page 6

page 7

page 8

page 9


SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

A key limitation of deep convolutional neural networks (DCNN) based imag...

Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

Simulating images representative of neurodegenerative diseases is import...

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease

Parkinson's Disease (PD) is one of the most prevalent neurodegenerative ...

A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images

Background: Parkinson's disease (PD) is a prevalent long-term neurodegen...

Deep Learning Predicts Prevalent and Incident Parkinson's Disease From UK Biobank Fundus Imaging

Parkinson's disease is the world's fastest growing neurological disorder...

Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks

Radiographic images offer an alternative method for the rapid screening ...

Please sign up or login with your details

Forgot password? Click here to reset