Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification

Lalys, Florent; Riffaud, Laurent; Morandi, Xavier; Jannin, Pierre

doi:10.1007/978-3-642-13711-2_4

Cited by 29 publications

(28 citation statements)

References 21 publications

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“…Most of the prior work focuses on the detection of the instruments used during surgery or in the operating room [13,14,15,16,17,18,19,20] using techniques such as dynamic time warping, support vector machines and HMMs. However, these techniques use only frame-level features, such as color, texture and shape-based cues.…”

Section: Introductionmentioning

confidence: 99%

Surgical Gesture Segmentation and Recognition

Tao

Zappella

Hager

et al. 2013

Lecture Notes in Computer Science

103

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Abstract. Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.

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Section: Introductionmentioning

confidence: 99%

Surgical Gesture Segmentation and Recognition

Tao

Zappella

Hager

et al. 2013

Lecture Notes in Computer Science

103

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“…To solve this problem, an analysis of endoscopic and microscopic video has been proposed [31][32][33]. Markov models and dynamic time warping (DTW) were used to identify single worksteps based on the presence of surgical instruments [34][35][36][37], and radiofrequency identification (RFID), visual approaches, and weight analysis methods [38] have been employed to recognize instrument use [37,39,40].…”

Section: Data Acquisition By Sensor Systemsmentioning

confidence: 99%

“…All of these approaches address different levels of granularity from ranged gesture recognition (surgemes) [42,44,45] to low-level tasks [33], high-level tasks [46,47], and intervention phases [35,48]. James et al [49] tried to recognize the current surgical situation indirectly by estimating the positions and movements of the members of surgical teams within the OR or by deriving information from other indirect features.…”

Section: Data Acquisition By Sensor Systemsmentioning

confidence: 99%

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Surgical process modeling

Neumuth

2017

Innovative Surgical Sciences

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Due to the rapidly evolving medical, technological, and technical possibilities, surgical procedures are becoming more and more complex. On the one hand, this offers an increasing number of advantages for patients, such as enhanced patient safety, minimal invasive interventions, and less medical malpractices. On the other hand, it also heightens pressure on surgeons and other clinical staff and has brought about a new policy in hospitals, which must rely on a great number of economic, social, psychological, qualitative, practical, and technological resources. As a result, medical disciplines, such as surgery, are slowly merging with technical disciplines. However, this synergy is not yet fully matured. The current information and communication technology in hospitals cannot manage the clinical and operational sequence adequately. The consequences are breaches in the surgical workflow, extensions in procedure times, and media disruptions. Furthermore, the data accrued in operating rooms (ORs) by surgeons and systems are not sufficiently implemented. A flood of information, "big data", is available from information systems. That might be deployed in the context of Medicine 4.0 to facilitate the surgical treatment. However, it is unused due to infrastructure breaches or communication errors. Surgical process models (SPMs) alleviate these problems. They can be defined as simplified, formal, or semiformal representations of a network of surgery-related activities, reflecting a predefined subset of interest. They can employ different means of generation, languages, and data acquisition strategies. They can represent surgical interventions with high resolution, offering qualifiable and quantifiable information on the course of the intervention on the level of single, minute, surgical work-steps. The basic idea is to gather information concerning the surgical intervention and its activities, such as performance time, surgical instrument used, trajectories, movements, or intervention phases. These data can be gathered by means of workflow recordings.These recordings are abstracted to represent an individual surgical process as a model and are an essential requirement to enable Medicine 4.0 in the OR. Further abstraction can be generated by merging individual process models to form generic SPMs to increase the validity for a larger number of patients. Furthermore, these models can be applied in a wide variety of use-cases. In this regard, the term "modeling" can be used to support either one or more of the following tasks: "to describe", "to understand", "to explain", to optimize", "to learn", "to teach", or "to automate". Possible use-cases are requirements analyses, evaluating surgical assist systems, generating surgeon-specific training-recommendation, creating workflow management systems for ORs, and comparing different surgical strategies. The presented chapter will give an introduction into this challenging topic, presenting different methods to generate SPMs from the workflow in the OR, as well as various...

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“…We already presented this approach in a previous paper [12]. Each frame was represented by a signature composed of low-level spatial features (RGB space, co-occurrence matrix with Haralick descriptors [13], spatial moments [14], and Discrete Cosine Transform (DCT) [15] coefficients).…”

Section: Other Visual Cuesmentioning

confidence: 99%

An Application-Dependent Framework for the Recognition of High-Level Surgical Tasks in the OR

Lalys

Riffaud

Bouget

et al. 2011

Lecture Notes in Computer Science

Self Cite

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Abstract. Surgical process analysis and modeling is a recent and important topic aiming at introducing a new generation of computer-assisted surgical systems. Among all of the techniques already in use for extracting data from the Operating Room, the use of image videos allows automating the surgeons' assistance without altering the surgical routine. We proposed in this paper an application-dependent framework able to automatically extract the phases of the surgery only by using microscope videos as input data and that can be adaptable to different surgical specialties. First, four distinct types of classifiers based on image processing were implemented to extract visual cues from video frames. Each of these classifiers was related to one kind of visual cue: visual cues recognizable through color were detected with a color histogram approach, for shape-oriented visual cues we trained a Haar classifier, for texture-oriented visual cues we used a bag-of-word approach with SIFT descriptors, and for all other visual cues we used a classical image classification approach including a feature extraction, selection, and a supervised classification. The extraction of this semantic vector for each video frame then permitted to classify time series using either Hidden Markov Model or Dynamic Time Warping algorithms. The framework was validated on cataract surgeries, obtaining accuracies of 95%.

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Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification

Cited by 29 publications

References 21 publications

Surgical Gesture Segmentation and Recognition

Surgical Gesture Segmentation and Recognition

Surgical process modeling

An Application-Dependent Framework for the Recognition of High-Level Surgical Tasks in the OR

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