Informative frame classification for endoscopy video

Oh, Jung Hwan; Hwang, Sae; Lee, Jeong Kyu; Tavanapong, Wallapak; Wong, Johnny; Groen, Piet C. de

doi:10.1016/j.media.2006.10.003

Cited by 101 publications

(92 citation statements)

References 34 publications

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“…First, sometimes uninformative images are wrongly detected, which results in incorrect embedding in manifolds; e.g., an image with bubbles can never find a correct correspondence in eigenspaces. In the future, we will improve uninformative frame detection by the work of Atasoy et al [7] or the methods presented in [8,9]. Next, intrabronchus images are wrongly classified.…”

Section: Resultsmentioning

confidence: 99%

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

Luo

Kitasaka

Mori

2011

Lecture Notes in Computer Science

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Abstract. This paper presents a new bronchoscope motion tracking method that utilizes manifold modeling and sequential Monte Carlo (SMC) sampler to boost navigated bronchoscopy. Our strategy to estimate the bronchoscope motions comprises two main stages:(1) bronchoscopic scene identification and (2) SMC sampling. We extend a spatial local and global regressive mapping (LGRM) method to Spatial-LGRM to learn bronchoscopic video sequences and construct their manifolds. By these manifolds, we can classify bronchoscopic scenes to bronchial branches where a bronchoscope is located. Next, we employ a SMC sampler based on a selective image similarity measure to integrate estimates of stage (1) to refine positions and orientations of a bronchoscope. Our proposed method was validated on patient datasets. Experimental results demonstrate the effectiveness and robustness of our method for bronchoscopic navigation without an additional position sensor.

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Section: Resultsmentioning

confidence: 99%

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

Luo

Kitasaka

Mori

2011

Lecture Notes in Computer Science

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“…These works include (i) pre-processing of images such as image enhancement [14,41] and content filtering [2,36], (ii) real-time support at procedure time such as diagnostic decision support and computer-integrated surgery [44,45], as well as (iii) post-procedural applications such as quality/skills assessment [31,51] and contentbased retrieval [47,48]. A broad overview of such works is provided in an extensive survey by Muenzer et al [35].…”

Section: Related Workmentioning

confidence: 99%

Video retrieval in laparoscopic video recordings with dynamic content descriptors

Schoeffmann

Husslein

Kletz

et al. 2017

Multimed Tools Appl

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In the domain of gynecologic surgery an increasing number of surgeries are performed in a minimally invasive manner. These laparoscopic surgeries require specific psychomotor skills of the operating surgeon, which are difficult to learn and teach. This is the reason why an increasing number of surgeons promote checking video recordings of laparoscopic surgeries for the occurrence of technical errors with surgical actions. This manual surgical quality assessment (SQA) process, however, is very cumbersome and timeconsuming when carried out without any support from content-based video retrieval. Appl (2018) 77:16813-16832 Descriptor) that can be effectively used to find similar segments in a laparoscopic video database and thereby help surgeons to more quickly inspect other instances of a given error scene. We evaluate the retrieval performance of MIDD with surgical actions from gynecologic surgery in direct comparison to several other dynamic content descriptors. We show that the MIDD descriptor significantly outperforms the state-of-the-art in terms of retrieval performance as well as in terms of runtime performance. Additionally, we release the manually created video dataset of 16 classes of surgical actions from medical laparoscopy to the public, for further evaluations.

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“…Early techniques focused on cut-boundary detection or image grouping using pixel differences, histogram comparisons, edge differences, motion analysis and the like, while more recent methods such as presented in [5] have also used image similarity metrics, classification and clustering to achieve the same goal. In some applications as in [11,12], the problem of temporal video segmentation may be reformulated as a classification problem that distinguishes between informative and noise images.…”

Section: Temporal Segmentation From Kinematic Stabilitymentioning

confidence: 99%

Semi-automated Query Construction for Content-Based Endomicroscopy Video Retrieval

Tafreshi

Linard

André

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. Content-based video retrieval has shown promising results to help physicians in their interpretation of medical videos in general and endomicroscopic ones in particular. Defining a relevant query for CBVR can however be a complex and time-consuming task for non-expert and even expert users. Indeed, uncut endomicroscopy videos may very well contain images corresponding to a variety of different tissue types. Using such uncut videos as queries may lead to drastic performance degradations for the system. In this study, we propose a semi-automated methodology that allows the physician to create meaningful and relevant queries in a simple and efficient manner. We believe that this will lead to more reproducible and more consistent results. The validation of our method is divided into two approaches. The first one is an indirect validation based on per video classification results with histopathological ground-truth. The second one is more direct and relies on perceived inter-video visual similarity ground-truth. We demonstrate that our proposed method significantly outperforms the approach with uncut videos and approaches the performance of a tedious manual query construction by an expert. Finally, we show that the similarity perceived between videos by experts is significantly correlated with the inter-video similarity distance computed by our retrieval system.

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Informative frame classification for endoscopy video

Cited by 101 publications

References 34 publications

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

Video retrieval in laparoscopic video recordings with dynamic content descriptors

Semi-automated Query Construction for Content-Based Endomicroscopy Video Retrieval

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