A novel method for artery detection in laparoscopic surgery

Akbari, Hamed; Kosugi, Yukio; Kihara, Kazunori

doi:10.1007/s00464-007-9688-8

Cited by 17 publications

(27 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing this work to our previous report [1], we have demonstrated an increase in the sensitivity to 100% (from 95%), and the specificity has increased to 96% (from 92%). In addition, this work uses several different laparoscopic surgeries occurring in different patients, compared to the use of simulated data and the limited number of surgeries (only 2) used in the previous report [1].…”

Section: Discussionsupporting

confidence: 72%

“…In addition, this work uses several different laparoscopic surgeries occurring in different patients, compared to the use of simulated data and the limited number of surgeries (only 2) used in the previous report [1]. It may be possible to decrease the calculation time using super computers or parallel processing.…”

Section: Discussionmentioning

confidence: 99%

“…The surgeon can also feel for vessel elasticity with this surgical procedure. In contrast, in laparoscopic procedures, surgeons almost always face the problem of detecting and localizing blood vessels, whether trying to avoid Injury to major vessels in laparoscopic surgery is a serious and life-threatening complication that has been previously reported and reviewed by many different groups [1]- [6]. Differentiation of arteries from other structures and the ability to independently detect them has a variety of potential applications in the head, neck, lungs, heart, abdomen, and lower extremities.…”

Section: Introductionmentioning

confidence: 99%

“…Optical methods are attractive for identifying arteries in laparoscopic surgery mainly because light delivery to and from tissue can be accomplished via fiber optics [1]. As arteries are the only pulsatile objects in our system, we use this property to detect arteries or differentiate them from veins.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection

Akbari

Kosugi

Kojima

2009

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARYIn laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.

show abstract

Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection

Akbari

Kosugi

Kojima

2009

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The advantages of using MIS include smaller incision size, less pain during the recovery period, reduced blood loss, faster recovery time, shorter hospitalization periods, fewer complications due to infection, better cosmetic results due to less scarring, and improved treatment at reduced cost [1,2]. However, MIS suffers from the loss of tactile sensation and reduced location perception since the surgeon is not allowed to directly palpate the tissue, which can lead to risks including poor surgical outcomes and failure to differentiate target tissues from other tissues [3,4]. For these reasons, it is necessary to use a tactile sensor for MIS.…”

Section: Introductionmentioning

confidence: 99%

Tissue characterization using an acoustic wave tactile sensor array

Kim

Jiang

2013

SPIE Proceedings

View full text Add to dashboard Cite

Tactile perception of different types of tissue is important in order for surgeons to perform procedures correctly and safely. This is especially true in minimally invasive surgery (MIS) where the surgeon must be able to locate the target tissue without a direct line of sight or direct finger touch. In this study, tissue characterization using an acoustic wave tactile sensor array was investigated. This type of tactile sensor array can detect the acoustic impedance change of target materials. Abnormal tissues can have different Young's moduli and shear moduli caused by composition change compared to those of healthy tissues. This also leads to a difference in acoustic impedance which can be detected using our sensor array. The array was fabricated using a face-shear mode PMN-PT piezoelectric resonator which is highly sensitive to acoustic impedance load. Gelatin and water mixtures with weight concentration of 5 wt % -30 wt % were prepared as tissue phantoms. The shear modulus of each phantom was measured using bulk face-shear mode crystal resonators, and it was found that shear modulus change from 120 kPa to 430 kPa resulted on 30 % electrical impedance shift from the resonator. Imaging display of elastic properties of prepared phantoms was also tested using the fabricated sensor array. The proposed tissue characterization technique is promising for the development of effective surgical procedures in minimally invasive surgery.

show abstract

Multi‐instance multi‐label learning for surgical image annotation

Loukas

Sgouros

2019

Robotics Computer Surgery

View full text Add to dashboard Cite

Background Various techniques have been proposed in the literature for phase and tool recognition from laparoscopic videos. In comparison, research in multilabel annotation of still frames is limited. Methods We describe a framework for multilabel annotation of images extracted from laparoscopic cholecystectomy (LC) videos based on multi‐instance multiple‐label learning. The image is considered as a bag of features extracted from local regions after coarse segmentation. A method based on variational Bayesian gaussian mixture models (VBGMM) is proposed for bag representation. Three techniques based on different feature extraction and bag representation models are employed for comparison. Results Four anatomical structures (abdominal wall, gallbladder, fat, and liver bed) and a tool‐like object (specimen bag) were annotated in 482 images. Our method achieved the best performance on single label accuracy: 0.87 (highest) and 0.69 (lowest). Moreover, the performance was >20% higher in terms of four multilabel classification error metrics (one‐error, ranking‐loss, hamming‐loss, and coverage). Conclusions Our approach provides an accurate and efficient image representation for multilabel classification of still images captured in LC.

show abstract

A novel method for artery detection in laparoscopic surgery

Abstract: This method is a noninvasive, reliable, and cost-effective technique to detect artery regions, even if some of them are covered with fat or other tissues, while suppressing veins and other tissues.

Cited by 17 publications

References 21 publications

Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection

Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection

Tissue characterization using an acoustic wave tactile sensor array

Multi‐instance multi‐label learning for surgical image annotation

Contact Info

Product

Resources

About