1
|
Rueckert T, Rueckert D, Palm C. Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art. Comput Biol Med 2024; 169:107929. [PMID: 38184862 DOI: 10.1016/j.compbiomed.2024.107929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/02/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.
Collapse
Affiliation(s)
- Tobias Rueckert
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany.
| | - Daniel Rueckert
- Artificial Intelligence in Healthcare and Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany; Department of Computing, Imperial College London, UK
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany; Regensburg Center of Health Sciences and Technology (RCHST), OTH Regensburg, Germany
| |
Collapse
|
2
|
Rashidi Fathabadi F, Grantner JL, Shebrain SA, Abdel-qader I. Autonomous sequential surgical skills assessment for the peg transfer task in a laparoscopic box-trainer system with three cameras. ROBOTICA 2023. [DOI: 10.1017/s0263574723000218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Abstract
In laparoscopic surgery, surgeons should develop several manual laparoscopic skills before carrying out real operative procedures using a low-cost box trainer. The Fundamentals of Laparoscopic Surgery (FLS) program was developed as a program to assess fundamental knowledge and surgical skills, required for basic laparoscopic surgery. The peg transfer task is a hands-on exam in the FLS program that assists a trainee to understand the relative minimum amount of grasping force necessary to move the pegs from one place to another place without dropping them. In this paper, an autonomous, sequential assessment algorithm based on deep learning, a multi-object detection method, and, several sequential If-Then conditional statements have been developed to monitor each step of a surgeon’s performance. Images from three different cameras are used to assess whether the surgeon executes the peg transfer task correctly and to display a notification on any errors on the monitor immediately. This algorithm improves the performance of a laparoscopic box-trainer system using top, side, and front cameras and removes the need for any human monitoring during a peg transfer task. The developed algorithm can detect each object and its status during a peg transfer task and notifies the resident about the correct or failed outcome. In addition, this system can correctly determine the peg transfer execution time, and the move, carry, and dropped states for each object by the top, side, and front-mounted cameras. Based on the experimental results, the proposed surgical skill assessment system can identify each object at a high score of fidelity, and the train-validation total loss for the single-shot detector (SSD) ResNet50 v1 was about 0.05. Also, the mean average precision (mAP) and Intersection over Union (IoU) of this detection system were 0.741, and 0.75, respectively. This project is a collaborative research effort between the Department of Electrical and Computer Engineering and the Department of Surgery, at Western Michigan University.
Collapse
|
3
|
Nespolo RG, Yi D, Cole E, Wang D, Warren A, Leiderman YI. Feature Tracking and Segmentation in Real Time via Deep Learning in Vitreoretinal Surgery: A Platform for Artificial Intelligence-Mediated Surgical Guidance. Ophthalmol Retina 2023; 7:236-242. [PMID: 36241132 DOI: 10.1016/j.oret.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/28/2022] [Accepted: 10/03/2022] [Indexed: 11/15/2022]
Abstract
PURPOSE This study investigated whether a deep-learning neural network can detect and segment surgical instrumentation and relevant tissue boundaries and landmarks within the retina using imaging acquired from a surgical microscope in real time, with the goal of providing image-guided vitreoretinal (VR) microsurgery. DESIGN Retrospective analysis via a prospective, single-center study. PARTICIPANTS One hundred and one patients undergoing VR surgery, inclusive of core vitrectomy, membrane peeling, and endolaser application, in a university-based ophthalmology department between July 1, 2020, and September 1, 2021. METHODS A dataset composed of 606 surgical image frames was annotated by 3 VR surgeons. Annotation consisted of identifying the location and area of the following features, when present in-frame: vitrector-, forceps-, and endolaser tooltips, optic disc, fovea, retinal tears, retinal detachment, fibrovascular proliferation, endolaser spots, area where endolaser was applied, and macular hole. An instance segmentation fully convolutional neural network (YOLACT++) was adapted and trained, and fivefold cross-validation was employed to generate metrics for accuracy. MAIN OUTCOME MEASURES Area under the precision-recall curve (AUPR) for the detection of elements tracked and segmented in the final test dataset; the frames per second (FPS) for the assessment of suitability for real-time performance of the model. RESULTS The platform detected and classified the vitrector tooltip with a mean AUPR of 0.972 ± 0.009. The segmentation of target tissues, such as the optic disc, fovea, and macular hole reached mean AUPR values of 0.928 ± 0.013, 0.844 ± 0.039, and 0.916 ± 0.021, respectively. The postprocessed image was rendered at a full high-definition resolution of 1920 × 1080 pixels at 38.77 ± 1.52 FPS when attached to a surgical visualization system, reaching up to 87.44 ± 3.8 FPS. CONCLUSIONS Neural Networks can localize, classify, and segment tissues and instruments during VR procedures in real time. We propose a framework for developing surgical guidance and assessment platform that may guide surgical decision-making and help in formulating tools for systematic analyses of VR surgery. Potential applications include collision avoidance to prevent unintended instrument-tissue interactions and the extraction of spatial localization and movement of surgical instruments for surgical data science research. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
Collapse
Affiliation(s)
- Rogerio Garcia Nespolo
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois
| | - Darvin Yi
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Daniel Wang
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Alexis Warren
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Yannek I Leiderman
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois.
| |
Collapse
|
4
|
Nespolo RG, Cole E, Wang D, Yi D, Leiderman YI. A Platform for Tracking Surgeon and Observer Gaze as a Surrogate for Attention in Ophthalmic Surgery. Ophthalmol Sci 2022; 3:100246. [PMID: 36748062 PMCID: PMC9898791 DOI: 10.1016/j.xops.2022.100246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/25/2022] [Indexed: 11/10/2022]
Abstract
Purpose To develop and validate a platform that can extract eye gaze metrics from surgeons observing cataract and vitreoretinal procedures and to enable post hoc data analysis to assess potential discrepancies in eye movement behavior according to surgeon experience. Design Experimental, prospective, single-center study. Participants Eleven ophthalmic surgeons observing deidentified vitreoretinal and cataract surgical procedures performed at a single university-based medical center. Methods An open-source platform was developed to extract gaze coordinates and metrics from ophthalmic surgeons via a computer vision algorithm in conjunction with a neural network to track and segment instruments and tissues, identifying areas of attention in the visual field of study subjects. Eleven surgeons provided validation data by watching videos of 6 heterogeneous vitreoretinal and cataract surgical phases. Main Outcome Measures Accuracy and distance traveled by the eye gaze of participants and overlap of the participants' eye gaze with instruments and tissues while observing surgical procedures. Results The platform demonstrated repeatability of > 94% when acquiring the eye gaze behavior of subjects. Attending ophthalmic surgeons and clinical fellows exhibited a lower overall cartesian distance traveled in comparison to resident physicians in ophthalmology (P < 0.02). Ophthalmology residents and clinical fellows exhibited more fixations to the display area where surgical device parameters were superimposed than attending surgeons (P < 0.05). There was a trend toward gaze overlap with the instrument tooltip location among resident physicians in comparison to attending surgeons and fellows (41.42% vs. 34.8%, P > 0.2). The number and duration of fixations did not vary substantially among groups (P > 0.3). Conclusions The platform proved effective in extracting gaze metrics of ophthalmic surgeons. These preliminary data suggest that surgeon gaze behavior differs according to experience.
Collapse
Affiliation(s)
- Rogerio G. Nespolo
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois,Richard and Loan Hill Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois
| | - Emily Cole
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois
| | - Daniel Wang
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois
| | - Darvin Yi
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois,Richard and Loan Hill Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois
| | - Yannek I. Leiderman
- Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois,Richard and Loan Hill Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois,Correspondence: Yannek I. Leiderman, MD, PhD, University of Illinois, 1905 W. Taylor St, Chicago, IL 60612.
| |
Collapse
|
5
|
Rodrigues M, Mayo M, Patros P. Surgical Tool Datasets for Machine Learning Research: A Survey. Int J Comput Vis. [DOI: 10.1007/s11263-022-01640-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AbstractThis paper is a comprehensive survey of datasets for surgical tool detection and related surgical data science and machine learning techniques and algorithms. The survey offers a high level perspective of current research in this area, analyses the taxonomy of approaches adopted by researchers using surgical tool datasets, and addresses key areas of research, such as the datasets used, evaluation metrics applied and deep learning techniques utilised. Our presentation and taxonomy provides a framework that facilitates greater understanding of current work, and highlights the challenges and opportunities for further innovative and useful research.
Collapse
|
6
|
Tang EM, El-Haddad MT, Patel SN, Tao YK. Automated instrument-tracking for 4D video-rate imaging of ophthalmic surgical maneuvers. Biomed Opt Express 2022; 13:1471-1484. [PMID: 35414968 PMCID: PMC8973184 DOI: 10.1364/boe.450814] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 05/11/2023]
Abstract
Intraoperative image-guidance provides enhanced feedback that facilitates surgical decision-making in a wide variety of medical fields and is especially useful when haptic feedback is limited. In these cases, automated instrument-tracking and localization are essential to guide surgical maneuvers and prevent damage to underlying tissue. However, instrument-tracking is challenging and often confounded by variations in the surgical environment, resulting in a trade-off between accuracy and speed. Ophthalmic microsurgery presents additional challenges due to the nonrigid relationship between instrument motion and instrument deformation inside the eye, image field distortion, image artifacts, and bulk motion due to patient movement and physiological tremor. We present an automated instrument-tracking method by leveraging multimodal imaging and deep-learning to dynamically detect surgical instrument positions and re-center imaging fields for 4D video-rate visualization of ophthalmic surgical maneuvers. We are able to achieve resolution-limited tracking accuracy at varying instrument orientations as well as at extreme instrument speeds and image defocus beyond typical use cases. As proof-of-concept, we perform automated instrument-tracking and 4D imaging of a mock surgical task. Here, we apply our methods for specific applications in ophthalmic microsurgery, but the proposed technologies are broadly applicable for intraoperative image-guidance with high speed and accuracy.
Collapse
Affiliation(s)
- Eric M. Tang
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN 37232, USA
| | - Mohamed T. El-Haddad
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN 37232, USA
| | - Shriji N. Patel
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yuankai K. Tao
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN 37232, USA
| |
Collapse
|
7
|
Gkouzionis I, Nazarian S, Kawka M, Darzi A, Patel N, Peters CJ, Elson DS. Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery. J Biomed Opt 2022; 27:JBO-210293R. [PMID: 35106980 PMCID: PMC8804336 DOI: 10.1117/1.jbo.27.2.025001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/10/2022] [Indexed: 05/27/2023]
Abstract
SIGNIFICANCE Diffuse reflectance spectroscopy (DRS) allows discrimination of tissue type. Its application is limited by the inability to mark the scanned tissue and the lack of real-time measurements. AIM This study aimed to develop a real-time tracking system to enable localization of a DRS probe to aid the classification of tumor and non-tumor tissue. APPROACH A green-colored marker attached to the DRS probe was detected using hue-saturation-value (HSV) segmentation. A live, augmented view of tracked optical biopsy sites was recorded in real time. Supervised classifiers were evaluated in terms of sensitivity, specificity, and overall accuracy. A developed software was used for data collection, processing, and statistical analysis. RESULTS The measured root mean square error (RMSE) of DRS probe tip tracking was 1.18 ± 0.58 mm and 1.05 ± 0.28 mm for the x and y dimensions, respectively. The diagnostic accuracy of the system to classify tumor and non-tumor tissue in real time was 94% for stomach and 96% for the esophagus. CONCLUSIONS We have successfully developed a real-time tracking and classification system for a DRS probe. When used on stomach and esophageal tissue for tumor detection, the accuracy derived demonstrates the strength and clinical value of the technique to aid margin assessment in cancer resection surgery.
Collapse
Affiliation(s)
- Ioannis Gkouzionis
- Imperial College London, Department of Surgery and Cancer, London, United Kingdom
- Imperial College London, Hamlyn Centre, London, United Kingdom
| | - Scarlet Nazarian
- Imperial College London, Department of Surgery and Cancer, London, United Kingdom
| | - Michal Kawka
- Imperial College London, Department of Surgery and Cancer, London, United Kingdom
| | - Ara Darzi
- Imperial College London, Department of Surgery and Cancer, London, United Kingdom
- Imperial College London, Hamlyn Centre, London, United Kingdom
| | - Nisha Patel
- Imperial College London, Department of Surgery and Cancer, London, United Kingdom
| | | | - Daniel S. Elson
- Imperial College London, Department of Surgery and Cancer, London, United Kingdom
- Imperial College London, Hamlyn Centre, London, United Kingdom
| |
Collapse
|
8
|
Loeza-Mejía CI, Sánchez-DelaCruz E, Pozos-Parra P, Landero-Hernández LA. The potential and challenges of Health 4.0 to face COVID-19 pandemic: a rapid review. Health Technol (Berl) 2021; 11:1321-1330. [PMID: 34603926 PMCID: PMC8477175 DOI: 10.1007/s12553-021-00598-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/14/2021] [Indexed: 11/05/2022]
Abstract
The COVID-19 pandemic has generated the need to evolve health services to reduce the risk of contagion and promote a collaborative environment even remotely. Advances in Industry 4.0, including the internet of things, mobile networks, cloud computing, and artificial intelligence make Health 4.0 possible to connect patients with healthcare professionals. Hence, the focus of this work is analyzing the potentiality, and challenges of state-of-the-art Health 4.0 applications to face the COVID-19 pandemic including augmented environments, diagnosis of the virus, forecasts, medical robotics, and remote clinical services. It is concluded that Health 4.0 can be applied in the prevention of contagion, improve diagnosis, promote virtual learning environments, and offer remote services. However, there are still ethical, technical, security, and legal challenges to be addressed. Additionally, more imaging datasets for COVID-19 detection need to be made available to the scientific community. Working in the areas of opportunity will help to address the new normal. Likewise, Health 4.0 can be applied not only in the COVID-19 pandemic, but also in future global viruses and natural disasters.
Collapse
|
9
|
Hou Y, Zhang W, Liu Q, Ge H, Meng J, Zhang Q, Wei X. Adaptive kernel selection network with attention constraint for surgical instrument classification. Neural Comput Appl 2021;:1-15. [PMID: 34539089 DOI: 10.1007/s00521-021-06368-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/26/2021] [Indexed: 11/15/2022]
Abstract
Computer vision (CV) technologies are assisting the health care industry in many respects, i.e., disease diagnosis. However, as a pivotal procedure before and after surgery, the inventory work of surgical instruments has not been researched with the CV-powered technologies. To reduce the risk and hazard of surgical tools’ loss, we propose a study of systematic surgical instrument classification and introduce a novel attention-based deep neural network called SKA-ResNet which is mainly composed of: (a) A feature extractor with selective kernel attention module to automatically adjust the receptive fields of neurons and enhance the learnt expression and (b) A multi-scale regularizer with KL-divergence as the constraint to exploit the relationships between feature maps. Our method is easily trained end-to-end in only one stage with few additional calculation burdens. Moreover, to facilitate our study, we create a new surgical instrument dataset called SID19 (with 19 kinds of surgical tools consisting of 3800 images) for the first time. Experimental results show the superiority of SKA-ResNet for the classification of surgical tools on SID19 when compared with state-of-the-art models. The classification accuracy of our method reaches up to 97.703%, which is well supportive for the inventory and recognition study of surgical tools. Also, our method can achieve state-of-the-art performance on four challenging fine-grained visual classification datasets.
Collapse
|
10
|
Abstract
PURPOSE OF REVIEW Artificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach, and aid the surgeon in all phases of surgical management. The purpose of this review is to highlight the ever-increasing intersection of computer vision, machine learning, and ophthalmic microsurgery. RECENT FINDINGS Deep learning algorithms are being applied to help evaluate and teach surgical trainees. Artificial intelligence tools are improving real-time surgical instrument tracking, phase segmentation, as well as enhancing the safety of robotic-assisted vitreoretinal surgery. SUMMARY Similar to strides appreciated in ophthalmic medical disease, artificial intelligence will continue to become an important part of surgical management of ocular conditions. Machine learning applications will help push the boundaries of what surgeons can accomplish to improve patient outcomes.
Collapse
Affiliation(s)
- Kapil Mishra
- Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California, USA
| | | |
Collapse
|
11
|
Pose Díez de la Lastra A, García-Duarte Sáenz L, García-Mato D, Hernández-Álvarez L, Ochandiano S, Pascau J. Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling. Entropy (Basel) 2021; 23:e23070817. [PMID: 34206962 PMCID: PMC8303376 DOI: 10.3390/e23070817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/09/2021] [Accepted: 06/23/2021] [Indexed: 01/09/2023]
Abstract
Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.
Collapse
Affiliation(s)
- Alicia Pose Díez de la Lastra
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, 28911 Leganés, Spain; (A.P.D.d.l.L.); lucia.g.-@alumnos.uc3m.es (L.G.-D.S.); (D.G.-M.)
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain;
| | - Lucía García-Duarte Sáenz
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, 28911 Leganés, Spain; (A.P.D.d.l.L.); lucia.g.-@alumnos.uc3m.es (L.G.-D.S.); (D.G.-M.)
| | - David García-Mato
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, 28911 Leganés, Spain; (A.P.D.d.l.L.); lucia.g.-@alumnos.uc3m.es (L.G.-D.S.); (D.G.-M.)
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain;
| | - Luis Hernández-Álvarez
- Departamento de Tecnologías de la Información y las Comunicaciones (TIC), Instituto de Tecnologías Físicas y de la Información (ITEFI), Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain;
| | - Santiago Ochandiano
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain;
- Servicio de Cirugía Oral y Maxilofacial, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
| | - Javier Pascau
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, 28911 Leganés, Spain; (A.P.D.d.l.L.); lucia.g.-@alumnos.uc3m.es (L.G.-D.S.); (D.G.-M.)
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain;
- Correspondence: ; Tel.: +34-91-624-8196
| |
Collapse
|
12
|
Cho SM, Kim YG, Jeong J, Kim I, Lee HJ, Kim N. Automatic tip detection of surgical instruments in biportal endoscopic spine surgery. Comput Biol Med 2021; 133:104384. [PMID: 33864974 DOI: 10.1016/j.compbiomed.2021.104384] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/29/2021] [Accepted: 04/05/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND Recent advances in robotics and deep learning can be used in endoscopic surgeries and can provide numerous advantages by freeing one of the surgeon's hands. This study aims to automatically detect the tip of the instrument, localize a point, and evaluate the detection accuracy in biportal endoscopic spine surgery (BESS). The tip detection could serve as a preliminary study for the development of vision intelligence in robotic endoscopy. METHODS The dataset contains 2310 frames from 9 BESS videos with x and y coordinates of the tip annotated by an expert. We trained two state-of-the-art detectors, RetinaNet and YOLOv2, with bounding boxes centered around the tip annotations with specific margin sizes to determine the optimal margin size for detecting the tip of the instrument and localizing the point. We calculated the recall, precision, and F1-score with a fixed box size for both ground truth tip coordinates and predicted midpoints to compare the performance of the models trained with different margin size bounding boxes. RESULTS For RetinaNet, a margin size of 150 pixels was optimal with a recall of 1.000, precision of 0.733, and F1-score of 0.846. For YOLOv2, a margin size of 150 pixels was optimal with a recall of 0.864, precision of 0.808, F1-score of 0.835. Also, the optimal margin size of 150 pixels of RetinaNet was used to cross-validate its overall robustness. The resulting mean recall, precision, and F1-score were 1.000 ± 0.000, 0.767 ± 0.033, and 0.868 ± 0.022, respectively. CONCLUSIONS In this study, we evaluated an automatic tip detection method for surgical instruments in endoscopic surgery, compared two state-of-the-art detection algorithms, RetinaNet and YOLOv2, and validated the robustness with cross-validation. This method can be applied in different types of endoscopy tip detection.
Collapse
Affiliation(s)
- Sue Min Cho
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Young-Gon Kim
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jinhoon Jeong
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Inhwan Kim
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Ho-Jin Lee
- Department of Orthopaedic Surgery, Chungnam National University School of Medicine, Seoul, South Korea
| | - Namkug Kim
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.
| |
Collapse
|
13
|
Gültekin İB, Karabük E, Köse MF. "Hey Siri! Perform a type 3 hysterectomy. Please watch out for the ureter!" What is autonomous surgery and what are the latest developments? J Turk Ger Gynecol Assoc 2021; 22:58-70. [PMID: 33624493 PMCID: PMC7944239 DOI: 10.4274/jtgga.galenos.2021.2020.0187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
As a result of major advances in deep learning algorithms and computer processing power, there have been important developments in the fields of medicine and robotics. Although fully autonomous surgery systems where human impact will be minimized are still a long way off, systems with partial autonomy have gradually entered clinical use. In this review, articles on autonomous surgery classified and summarized, with the aim of informing the reader about questions such as "What is autonomic surgery?" and in which areas studies are progressing.
Collapse
Affiliation(s)
- İsmail Burak Gültekin
- Department of Obstetrics and Gynecology, University of Health Sciences, Dr. Sami Ulus Training and Research Hospital, Ankara, Turkey
| | - Emine Karabük
- Department of Obstetrics and Gynecology, Acıbadem University Faculty of Medicine, İstanbul, Turkey
| | - Mehmet Faruk Köse
- Department of Obstetrics and Gynecology, Acıbadem University Faculty of Medicine, İstanbul, Turkey
| |
Collapse
|
14
|
Roß T, Reinke A, Full PM, Wagner M, Kenngott H, Apitz M, Hempe H, Mindroc-Filimon D, Scholz P, Tran TN, Bruno P, Arbeláez P, Bian GB, Bodenstedt S, Bolmgren JL, Bravo-Sánchez L, Chen HB, González C, Guo D, Halvorsen P, Heng PA, Hosgor E, Hou ZG, Isensee F, Jha D, Jiang T, Jin Y, Kirtac K, Kletz S, Leger S, Li Z, Maier-Hein KH, Ni ZL, Riegler MA, Schoeffmann K, Shi R, Speidel S, Stenzel M, Twick I, Wang G, Wang J, Wang L, Wang L, Zhang Y, Zhou YJ, Zhu L, Wiesenfarth M, Kopp-Schneider A, Müller-Stich BP, Maier-Hein L. Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge. Med Image Anal 2020; 70:101920. [PMID: 33676097 DOI: 10.1016/j.media.2020.101920] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/22/2020] [Accepted: 11/24/2020] [Indexed: 12/27/2022]
Abstract
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).
Collapse
Affiliation(s)
- Tobias Roß
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany.
| | - Annika Reinke
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany
| | - Peter M Full
- University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany; Division of Medical Image Computing (MIC), Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Martin Wagner
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Hannes Kenngott
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Martin Apitz
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Hellena Hempe
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Diana Mindroc-Filimon
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Patrick Scholz
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; HIDSS4Health - Helmholtz Information and Data Science School for Health, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Thuy Nuong Tran
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Pierangela Bruno
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; Department of Mathematics and Computer Science, University of Calabria, 87036 Rende, Italy
| | - Pablo Arbeláez
- Universidad de los Andes, Cra. 1 No 18A - 12, 111711 Bogotá, Colombia
| | - Gui-Bin Bian
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Sebastian Bodenstedt
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Bautzner Landstraße 400, 01328 Dresden, Germany
| | | | | | - Hua-Bin Chen
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Cristina González
- Universidad de los Andes, Cra. 1 No 18A - 12, 111711 Bogotá, Colombia
| | - Dong Guo
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Shahe Campus:No.4, Section 2, North Jianshe Road, 610054
- Qingshuihe Campus:No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Pål Halvorsen
- SimulaMet, Pilestredet 52, 0167 Oslo, Norway; Oslo Metropolitan University (OsloMet), Pilestredet 52, 0167 Oslo, Norway
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Chung Chi Rd, Ma Liu Shui, Hong Kong, China
| | - Enes Hosgor
- caresyntax, Komturstraße 18A, 12099 Berlin, Germany
| | - Zeng-Guang Hou
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Fabian Isensee
- University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany; Division of Medical Image Computing (MIC), Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Debesh Jha
- SimulaMet, Pilestredet 52, 0167 Oslo, Norway; Department of Informatics, UIT The Arctic University of Norway, Hansine Hansens vei 54, 9037 Tromsø, Norway
| | - Tingting Jiang
- Institute of Digital Media (NELVT), Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Peking, China
| | - Yueming Jin
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Chung Chi Rd, Ma Liu Shui, Hong Kong, China
| | - Kadir Kirtac
- caresyntax, Komturstraße 18A, 12099 Berlin, Germany
| | - Sabrina Kletz
- Institute of Information Technology, Klagenfurt University, Universitätsstraße 65-67, 9020 Klagenfurt, Austria
| | - Stefan Leger
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Bautzner Landstraße 400, 01328 Dresden, Germany
| | - Zhixuan Li
- Institute of Digital Media (NELVT), Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Peking, China
| | - Klaus H Maier-Hein
- Division of Medical Image Computing (MIC), Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Zhen-Liang Ni
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | | | - Klaus Schoeffmann
- Institute of Information Technology, Klagenfurt University, Universitätsstraße 65-67, 9020 Klagenfurt, Austria
| | - Ruohua Shi
- Institute of Digital Media (NELVT), Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Peking, China
| | - Stefanie Speidel
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Bautzner Landstraße 400, 01328 Dresden, Germany
| | | | | | - Gutai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Shahe Campus:No.4, Section 2, North Jianshe Road, 610054
- Qingshuihe Campus:No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Jiacheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, 422 Siming South Road, 361005 Xiamen, China
| | - Liansheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, 422 Siming South Road, 361005 Xiamen, China
| | - Lu Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Shahe Campus:No.4, Section 2, North Jianshe Road, 610054
- Qingshuihe Campus:No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Yujie Zhang
- Department of Computer Science, School of Informatics, Xiamen University, 422 Siming South Road, 361005 Xiamen, China
| | - Yan-Jie Zhou
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Lei Zhu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Chung Chi Rd, Ma Liu Shui, Hong Kong, China
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, Germany
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, Germany
| | - Beat P Müller-Stich
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Lena Maier-Hein
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| |
Collapse
|
15
|
Tanzi L, Piazzolla P, Vezzetti E. Intraoperative surgery room management: A deep learning perspective. Int J Med Robot 2020; 16:1-12. [PMID: 32510857 DOI: 10.1002/rcs.2136] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/21/2020] [Accepted: 06/03/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE The current study aimed to systematically review the literature addressing the use of deep learning (DL) methods in intraoperative surgery applications, focusing on the data collection, the objectives of these tools and, more technically, the DL-based paradigms utilized. METHODS A literature search with classic databases was performed: we identified, with the use of specific keywords, a total of 996 papers. Among them, we selected 52 for effective analysis, focusing on articles published after January 2015. RESULTS The preliminary results of the implementation of DL in clinical setting are encouraging. Almost all the surgery sub-fields have seen the advent of artificial intelligence (AI) applications and the results outperformed the previous techniques in the majority of the cases. From these results, a conceptualization of an intelligent operating room (IOR) is also presented. CONCLUSION This evaluation outlined how AI and, in particular, DL are revolutionizing the surgery field, with numerous applications, such as context detection and room management. This process is evolving years by years into the realization of an IOR, equipped with technologies perfectly suited to drastically improve the surgical workflow.
Collapse
|
16
|
Sugimori H, Sugiyama T, Nakayama N, Yamashita A, Ogasawara K. Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument. Applied Sciences 2020; 10:4245. [DOI: 10.3390/app10124245] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This work aims to develop an algorithm to detect the distal end of a surgical instrument using object detection with deep learning. We employed nine video recordings of carotid endarterectomies for training and testing. We obtained regions of interest (ROI; 32 × 32 pixels), at the end of the surgical instrument on the video images, as supervised data. We applied data augmentation to these ROIs. We employed a You Only Look Once Version 2 (YOLOv2) -based convolutional neural network as the network model for training. The detectors were validated to evaluate average detection precision. The proposed algorithm used the central coordinates of the bounding boxes predicted by YOLOv2. Using the test data, we calculated the detection rate. The average precision (AP) for the ROIs, without data augmentation, was 0.4272 ± 0.108. The AP with data augmentation, of 0.7718 ± 0.0824, was significantly higher than that without data augmentation. The detection rates, including the calculated coordinates of the center points in the centers of 8 × 8 pixels and 16 × 16 pixels, were 0.6100 ± 0.1014 and 0.9653 ± 0.0177, respectively. We expect that the proposed algorithm will be efficient for the analysis of surgical records.
Collapse
|