Efficient Bronchoscopic Video Summarization

Byrnes, Patrick; Higgins, William E.

doi:10.1109/tbme.2018.2859322

Cited by 18 publications

(17 citation statements)

References 78 publications

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“…Steps 1-3 are performed using the video parsing approach of Byrnes and Higgins. 5 Step 4 is accomplished by Merritt et al 6 Since there can be some uninformative frames that have little use, the user can manually delete them to guarantee the registration process. 5 Further improvements can be made by detecting uninformative or excessive redundant video frames according to bronchoscope modality.…”

Section: Advanced Methodsmentioning

confidence: 99%

“…5 Step 4 is accomplished by Merritt et al 6 Since there can be some uninformative frames that have little use, the user can manually delete them to guarantee the registration process. 5 Further improvements can be made by detecting uninformative or excessive redundant video frames according to bronchoscope modality. 9,10 Given these registrations, we can now instead associate individual video frames from the various video sources to the virtual bronchoscope view sites along the CT-based airway tree.…”

Section: Advanced Methodsmentioning

confidence: 99%

“…Regarding step 1, we use previously validated methods to construct the chest model as highlighted in. 5 The airway-tree centerline structure spans the airways captured within the CT-based segmented airway tree, as shown in Fig. 1.…”

Section: Overview Of the Processmentioning

confidence: 99%

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Bronchoscopic video synchronization for interactive multimodal inspection of bronchial lesions

Chang,

Byrnes,

Ahmad

et al. 2023

Preprint

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With lung cancer being the most fatal cancer worldwide, it is important to detect the disease early. A potentially effective way of detecting early cancer lesions developing along the airway walls (epithelium) is bronchoscopy. To this end, developments in bronchoscopy offer three promising noninvasive modalities for imaging bronchial lesions: white-light bronchoscopy (WLB), autofluorescence bronchoscopy (AFB), and narrow-band imaging (NBI). While these modalities give complementary views of the airway epithelium, the physician must manually inspect each video stream produced by a given modality to locate the suspect cancer lesions. Unfortunately, no effort has been made to rectify this situation by providing efficient quantitative and visual tools for analyzing these video streams. This makes the lesion search process extremely time-consuming and error-prone, thereby making it impractical to utilize these rich data sources effectively. We propose a framework for synchronizing multiple bronchoscopic videos to enable interactive multimodal analysis of bronchial lesions. Our methods first register the video streams to a reference 3D chest computed-tomography (CT) scan to produce multimodal linkages to the airway tree. Our methods then temporally correlate the videos to one another to enable synchronous visualization of the resulting multimodal data set. Pictorial and quantitative results illustrate the potential of the methods.

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Section: Advanced Methodsmentioning

confidence: 99%

Section: Advanced Methodsmentioning

confidence: 99%

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Bronchoscopic video synchronization for interactive multimodal inspection of bronchial lesions

Chang,

Byrnes,

Ahmad

et al. 2023

Preprint

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“…Current usage of AFB forces the physician to search for lesions in a long, redundant video stream and rely on interactive visual criteria to detect lesions [4], [5], [7].…”

Section: Introductionmentioning

confidence: 99%

Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection

Chen

Bascom

Toth

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Because of the significance of bronchial lesions as indicators of early lung cancer and squamous cell carcinoma, a critical need exists for early detection of bronchial lesions. Autofluorescence bronchoscopy (AFB) is a primary modality used for bronchial lesion detection, as it shows high sensitivity to suspicious lesions. The physician, however, must interactively browse a long video stream to locate lesions, making the search exceedingly tedious and error prone. Unfortunately, limited research has explored the use of automated AFB video analysis for efficient lesion detection. We propose a robust automatic AFB analysis approach that distinguishes informative and uninformative AFB video frames in a video. In addition, for the informative frames, we determine the frames containing potential lesions and delineate candidate lesion regions. Our approach draws upon a combination of computer-based image analysis, machine learning, and deep learning. Thus, the analysis of an AFB video stream becomes more tractable. Tests with patient AFB video indicate that ≥97% of frames were correctly labeled as informative or uninformative. In addition, ≥97% of lesion frames were correctly identified, with false positive and false negative rates ≤3%.Clinical relevance-The method makes AFB-based bronchial lesion analysis more efficient, thereby helping to advance the goal of better early lung cancer detection.

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“…While Sorger et al [9] employed EM tracking for endobronchial ultrasound, Hofstad et al [10], [11] improved the registration between the EM system and preoperative images. As an alternative to electromagnetic tracking, Shen et al [12], [13] used depth estimation and recognition techniques to navigate the endoscope, while video summarization and classification were introduced to locate the endoscope [14], [15]. Additionally, a tabletop field generator (Northern Digital Inc., Waterloo, Canada) has been developed to incorporate a thin barrier that can minimize any tracking distortions caused by conductive or ferromagnetic materials.…”

Section: Introductionmentioning

confidence: 99%