Ye Menglong, Johns Edward, Walter Benjamin, Meining Alexander, Yang Guang-Zhong
The Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK.
Dyson Robotics Laboratory, Imperial College London, London, UK.
Int J Comput Assist Radiol Surg. 2017 Aug;12(8):1281-1292. doi: 10.1007/s11548-017-1620-7. Epub 2017 Jun 2.
Serial endoscopic examinations of a patient are important for early diagnosis of malignancies in the gastrointestinal tract. However, retargeting for optical biopsy is challenging due to extensive tissue variations between examinations, requiring the method to be tolerant to these changes whilst enabling real-time retargeting.
This work presents an image retrieval framework for inter-examination retargeting. We propose both a novel image descriptor tolerant of long-term tissue changes and a novel descriptor matching method in real time. The descriptor is based on histograms generated from regional intensity comparisons over multiple scales, offering stability over long-term appearance changes at the higher levels, whilst remaining discriminative at the lower levels. The matching method then learns a hashing function using random forests, to compress the string and allow for fast image comparison by a simple Hamming distance metric.
A dataset that contains 13 in vivo gastrointestinal videos was collected from six patients, representing serial examinations of each patient, which includes videos captured with significant time intervals. Precision-recall for retargeting shows that our new descriptor outperforms a number of alternative descriptors, whilst our hashing method outperforms a number of alternative hashing approaches.
We have proposed a novel framework for optical biopsy in serial endoscopic examinations. A new descriptor, combined with a novel hashing method, achieves state-of-the-art retargeting, with validation on in vivo videos from six patients. Real-time performance also allows for practical integration without disturbing the existing clinical workflow.
对患者进行系列内镜检查对于胃肠道恶性肿瘤的早期诊断至关重要。然而，由于不同检查之间组织变化广泛，光学活检的重新定位具有挑战性，这要求该方法能够耐受这些变化，同时实现实时重新定位。
这项工作提出了一种用于检查间重新定位的图像检索框架。我们提出了一种既耐受长期组织变化的新型图像描述符，又提出了一种实时的新型描述符匹配方法。该描述符基于在多个尺度上通过区域强度比较生成的直方图，在较高层次上对长期外观变化具有稳定性，而在较低层次上仍具有区分性。然后，匹配方法使用随机森林学习一个哈希函数，以压缩字符串并通过简单的汉明距离度量实现快速图像比较。
从6名患者收集了一个包含13个体内胃肠道视频的数据集，代表了对每位患者的系列检查，其中包括在显著时间间隔内拍摄的视频。重新定位的精确召回率表明，我们的新描述符优于许多其他描述符，而我们的哈希方法优于许多其他哈希方法。
我们提出了一种用于系列内镜检查中光学活检的新型框架。一种新的描述符与一种新型哈希方法相结合，实现了最先进的重新定位，并在6名患者的体内视频上得到了验证。实时性能还允许在不干扰现有临床工作流程的情况下进行实际整合。
