Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors

Nosrati, Masoud S.; Amir-Khalili, Alborz; Peyrat, Jean-Marc; Abinahed, Julien; Al-Alao, Osama; Al‐Ansari, Abdulla; Abugharbieh, Rafeef; Hamarneh, Ghassan

doi:10.1007/s11548-015-1331-x

Cited by 25 publications

(45 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nosrati et al. and Baghdadi et al. described ML‐based analysis of colour and textural visual features on robotic endoscopic view to detect anatomical landmarks during a partial nephrectomy and radical prostatectomy.…”

Section: Application Of Ai In Urologymentioning

confidence: 99%

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

View full text Add to dashboard Cite

Objective To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. Materials and methods A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and nonurologic studies were excluded. Results Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. Conclusion AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.

show abstract

Section: Application Of Ai In Urologymentioning

confidence: 99%

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To avoid confusion, we mention the term segmentation for finding contours and labeling for assigning pixel labels like as our task. The most similar task to ours is one conducted by Nosrati et al [21]. To increase a surgeon's visibility in an endoscopic view, they label visible objects (such as tumors, organs, and arteries) in an endoscopic video frame.…”

Section: Related Workmentioning

confidence: 99%

Discriminative Subtree Selection for NBI Endoscopic Image Labeling

Hirakawa

Tamaki

Kurita

et al. 2017

Computer Vision – ACCV 2016 Workshops

View full text Add to dashboard Cite

Abstract. In this paper, we propose a novel method for image labeling of colorectal Narrow Band Imaging (NBI) endoscopic images based on a tree of shapes. Labeling results could be obtained by simply classifying histogram features of all nodes in a tree of shapes, however, satisfactory results are difficult to obtain because histogram features of small nodes are not enough discriminative. To obtain discriminative subtrees, we propose a method that optimally selects discriminative subtrees. We model an objective function that includes the parameters of a classifier and a threshold to select subtrees. Then labeling is done by mapping the classification results of nodes of the subtrees to those corresponding image regions. Experimental results on a dataset of 63 NBI endoscopic images show that the proposed method performs qualitatively and quantitatively much better than existing methods.

show abstract

“…This problem, therefore, needs to be considered for further improvement to develop a more accurate AR system. This paper takes the best features from the work of Nosrati et al (2016) and focuses on increasing the system accuracy by removing the noise caused by patient respiration from the video frames The remainder of the paper is organized as follows: The section called "System Overview" describes the stateof-the-art method and details of the proposed technique including a system flowchart and pseudocode. The next section discusses result from the proposed technique and compares these with outcomes from the state-of-the-art method.…”

Section: Related Workmentioning

confidence: 99%

“…The system was proposed by Nosrati et al (2016) and delivers high accuracy in terms of visualizing blood vessels hidden by fat. It is also a cost-effective solution as there is no need for medical equipment such as markers that have been used in other current solutions.…”

Section: State Of Art Solutionmentioning

confidence: 99%

“…The state of art (Nosrati et al, 2016) process starts with segmenting the structures of interest from the 3D pre-operative data to create a 3D segmented model that can be aligned to the segmented 2D intraoperative data to provide an augmented view for the surgeon in the operating room during the endoscopic surgery. To deform the 3D pre-operative models of interest, intraoperative video frames are generalized using Eigen decomposition.…”

Section: Pre-operative Environmentmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Weighted Integral Energy Functional (WIEF) Algorithm: Augmented Reality (AR) for Visualising the Blood Vessels in Breast Implant Surgeries

Kaur

Alsadoon²,

Paul³

et al. 2018

American Journal of Applied Sciences

View full text Add to dashboard Cite

The use of Augmented Reality (AR) for visualising blood vessels in surgery is still at the experimental stage and has not been implemented due to limitations in terms of accuracy and processing time. The AR also hasn't applied in breast surgeries yet. As there is a need for a plastic surgeon to see the blood vessels before he cuts the breast and before putting the implant, this paper aims to improve the accuracy of augmented videos in visualising blood vessels during Breast Implant Surgery. The proposed system consists of a Weighted Integral Energy Functional (WIFE) algorithm to increase the accuracy of the augmented view in visualising the occluded blood vessels that covered by fat in the operating room. The results on breast area shows that the proposed algorithm improves video accuracy in terms of registration error to 0.32 mm and processing time to 23 sec compared to the state-of-the-art method. The proposed system focuses on increasing the accuracy in augmented view in visualising blood vessels during Breast Implant Surgery as it reduces the registration error. Thus, this study concentrates on looking at the feasibility of the use of Augmented Reality technology in Breast Augmentation surgeries.

show abstract

Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors

Cited by 25 publications

References 33 publications

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Discriminative Subtree Selection for NBI Endoscopic Image Labeling

A Novel Weighted Integral Energy Functional (WIEF) Algorithm: Augmented Reality (AR) for Visualising the Blood Vessels in Breast Implant Surgeries

Contact Info

Product

Resources

About