Robust Image Descriptors for Real-Time Inter-Examination Retargeting in Gastrointestinal Endoscopy
This work addresses the challenge of accurately relocating biopsy sites across endoscopic exams for early cancer diagnosis, representing an incremental improvement in medical imaging.
The authors tackled the problem of retargeting biopsy sites in serial gastrointestinal endoscopy exams by developing an image descriptor robust to tissue appearance changes over time, achieving superior performance compared to state-of-the-art methods on 13 in vivo videos from six patients.
For early diagnosis of malignancies in the gastrointestinal tract, surveillance endoscopy is increasingly used to monitor abnormal tissue changes in serial examinations of the same patient. Despite successes with optical biopsy for in vivo and in situ tissue characterisation, biopsy retargeting for serial examinations is challenging because tissue may change in appearance between examinations. In this paper, we propose an inter-examination retargeting framework for optical biopsy, based on an image descriptor designed for matching between endoscopic scenes over significant time intervals. Each scene is described by a hierarchy of regional intensity comparisons at various scales, offering tolerance to long-term change in tissue appearance whilst remaining discriminative. Binary coding is then used to compress the descriptor via a novel random forests approach, providing fast comparisons in Hamming space and real-time retargeting. Extensive validation conducted on 13 in vivo gastrointestinal videos, collected from six patients, show that our approach outperforms state-of-the-art methods.