合成胶囊内镜图像的临床多样性与合理性评估
Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images
AI总结：
本文提出基于StyleGAN的深度学习方法，利用多机构WCE数据生成具有可控临床属性和疾病进展场景的合成胶囊内镜图像图谱，并经胃肠病学专家主观评估验证其高度真实性与合理性。
摘要：
无线胶囊内镜（WCE）正日益被用作一种替代成像方式，用于对胃肠道进行完整且无创的筛查。尽管这有利于减少不必要的住院，但它也要求建立一套WCE诊断方案，以便能够对更大规模的人群进行有效筛查。这就需要专门针对该成像方式制定的培训与教育方案。与传统内镜、CT、MRI等其他成像方式的培训类似，WCE培训方案需要一本包含大量图像资料的图谱，这些图像应生动描述病理和异常表现，理想情况下应在一段时间内持续观察获得。由于目前WCE领域缺乏这样全面的图谱，在本工作中，我们提出了一种深度学习方法，利用不同机构已有的研究数据，通过StyleGAN创建真实的WCE图谱。我们识别出WCE中具有临床相关性的属性，从而能够按需生成具有选定属性的合成图像。除此之外，我们还模拟了多种疾病进展场景。通过三项主观在线实验，由来自三个地理位置、具有不同年限经验的八位胃肠病学专家参与，对生成图像的真实性和合理性进行了评估。实验结果表明，这些图像具有高度真实感，疾病场景也具有合理性。构成该图谱的图像已公开提供，可用于培训应用，也可作为真实数据集的补充用于深度学习。
英文摘要：
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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