Robust Image Descriptors for Real-Time Inter-Examination Retargeting in Gastrointestinal Endoscopy

Ye, Menglong; Johns, Edward; Walter, Benjamin; Meining, Alexander; Yang, Guang‐Zhong

doi:10.1007/978-3-319-46720-7_52

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Since there are soft tissue deformations, illumination changes, long time out of field-of-view(FOV), scale shifts and occlusion during the gastrointestinal biopsy of the digestive tract [20], [21], higher accuracy and robustness for tracking and repositioning ROI have been put forward. Universal tracking and repositioning frameworks are encountering significant challenges in intestinal gastrointestinal scenarios.…”

Section: An Overview Of the Conventional Tracking Methodsmentioning

confidence: 99%

Online Tracking and Relocation Based on a New Rotation-Invariant Haar-Like Statistical Descriptor in Endoscopic Examination

Gong

Chen

et al. 2020

IEEE Access

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In the gastrointestinal biopsy, online tracking and relocation of the region-of-interest are essential to early diagnosis and surgical intervention of colorectal cancer. However, it is challenging for the examiner to track and retarget the optical biopsy site due to interfering factors, e.g. violent rotation of the lens, illumination variation, shape deformation, and target long-time-lost. Previous works may not effectively handle the mentioned challenges due to the complexity of gastrointestinal environment and the limitation of data. In this work, we construct an online tracking and relocation framework based on the concept of detection and tracking, which is dramatically adapted to the inherent characteristics of the gastrointestinal biopsy image. To effectively distinguish the target area from the gastrointestinal biopsy, we designed a new rotated invariant Haar-like statistical descriptor which is robust for rotating and illumination changes. The descriptor is based on the sector-ring difference under the circular sampling area. A simplified statistical random forest discriminator based on confidence statistics is proposed to complete the preliminary screening of the potential tracking target. In order to further estimate the location of the target, a supervised support vector machine is introduced to rank the candidate target regions. Based on proposals of Siamese network and the random forest, a location refinement fusion has been proposed to determine the location and the confidence of the tracking area. Extensive experiments on various gastrointestinal videos, which consists of open source and self-collected data, demonstrate that the proposed framework is superior to the mainstreams methods in accuracy and robustness.

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Section: An Overview Of the Conventional Tracking Methodsmentioning

confidence: 99%

Online Tracking and Relocation Based on a New Rotation-Invariant Haar-Like Statistical Descriptor in Endoscopic Examination

Gong

Chen

et al. 2020

IEEE Access

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“…Furthermore, taking advantages of the noninvasive property, probe-based confocal laser endoscope (PCLE) is developed for assisting in quick detecting and positioning of polyps in real time. However, this technology relies on extra hardware solution and cannot return visit and review the lesions [9]. There is another research direction of lesion examination which uses panorama technology to expend the inner wall of soft organs.…”

Section: Introductionmentioning

confidence: 99%

Gastroscopic Panoramic View: Application to Automatic Polyps Detection under Gastroscopy

Shi

Xue

Jiang

et al. 2019

Computational and Mathematical Methods in Medicine

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Endoscopic diagnosis is an important means for gastric polyp detection. In this paper, a panoramic image of gastroscopy is developed, which can display the inner surface of the stomach intuitively and comprehensively. Moreover, the proposed automatic detection solution can help doctors locate the polyps automatically and reduce missed diagnosis. The main contributions of this paper are firstly, a gastroscopic panorama reconstruction method is developed. The reconstruction does not require additional hardware devices and can solve the problem of texture dislocation and illumination imbalance properly; secondly, an end-to-end multiobject detection for gastroscopic panorama is trained based on a deep learning framework. Compared with traditional solutions, the automatic polyp detection system can locate all polyps in the inner wall of the stomach in real time and assist doctors to find the lesions. Thirdly, the system was evaluated in the Affiliated Hospital of Zhejiang University. The results show that the average error of the panorama is less than 2 mm, the accuracy of the polyp detection is 95%, and the recall rate is 99%. In addition, the research roadmap of this paper has guiding significance for endoscopy-assisted detection of other human soft cavities.

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An image retrieval framework for real-time endoscopic image retargeting

Johns

Walter

et al. 2017

Int J CARS

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PurposeSerial 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.MethodThis 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.ResultsA 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.ConclusionWe 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.

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Robust Image Descriptors for Real-Time Inter-Examination Retargeting in Gastrointestinal Endoscopy

Cited by 4 publications

References 15 publications

Online Tracking and Relocation Based on a New Rotation-Invariant Haar-Like Statistical Descriptor in Endoscopic Examination

Online Tracking and Relocation Based on a New Rotation-Invariant Haar-Like Statistical Descriptor in Endoscopic Examination

Gastroscopic Panoramic View: Application to Automatic Polyps Detection under Gastroscopy

An image retrieval framework for real-time endoscopic image retargeting

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