scholarly journals Surgical workflow recognition with 3DCNN for Sleeve Gastrectomy

2021 ◽  
Author(s):  
Bokai Zhang ◽  
Amer Ghanem ◽  
Alexander Simes ◽  
Henry Choi ◽  
Andrew Yoo
Keyword(s):  
Neural Network ◽  
Sleeve Gastrectomy ◽  
Prior Knowledge ◽  
Imbalanced Data ◽  
Computer Assisted Surgery ◽  
Recognition Problem ◽  
Computer Assisted ◽  
Challenging Problem ◽  
Surgical Workflow ◽  
Data Problem

Abstract Purpose Surgical workflow recognition is a crucial and challenging problem when building a computer-assisted surgery system. Current techniques focus on utilizing a convolutional neural network and a recurrent neural network (CNN–RNN) to solve the surgical workflow recognition problem. In this paper, we attempt to use a deep 3DCNN to solve this problem. Methods In order to tackle the surgical workflow recognition problem and the imbalanced data problem, we implement a 3DCNN workflow referred to as I3D-FL-PKF. We utilize focal loss (FL) to train a 3DCNN architecture known as Inflated 3D ConvNet (I3D) for surgical workflow recognition. We use prior knowledge filtering (PKF) to filter the recognition results. Results We evaluate our proposed workflow on a large sleeve gastrectomy surgical video dataset. We show that focal loss can help to address the imbalanced data problem. We show that our PKF can be used to generate smoothed prediction results and improve the overall accuracy. We show that the proposed workflow achieves 84.16% frame-level accuracy and reaches a weighted Jaccard score of 0.7327 which outperforms traditional CNN–RNN design. Conclusion The proposed workflow can obtain consistent and smooth predictions not only within the surgical phases but also for phase transitions. By utilizing focal loss and prior knowledge filtering, our implementation of deep 3DCNN has great potential to solve surgical workflow recognition problems for clinical practice.

scholarly journals ClipAssistNet: bringing real-time safety feedback to operating rooms

2021 ◽  
Author(s):  
Florian Aspart ◽  
Jon L. Bolmgren ◽  
Joël L. Lavanchy ◽  
Guido Beldi ◽  
Michael S. Woods ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Laparoscopic Cholecystectomy ◽  
Real Time ◽  
Cystic Duct ◽  
Computer Assisted Surgery ◽  
Computer Assisted ◽  
Embedded Computing ◽  
Test Set ◽  
Critical Phase

Abstract Purpose Cholecystectomy is one of the most common laparoscopic procedures. A critical phase of laparoscopic cholecystectomy consists in clipping the cystic duct and artery before cutting them. Surgeons can improve the clipping safety by ensuring full visibility of the clipper, while enclosing the artery or the duct with the clip applier jaws. This can prevent unintentional interaction with neighboring tissues or clip misplacement. In this article, we present a novel real-time feedback to ensure safe visibility of the instrument during this critical phase. This feedback incites surgeons to keep the tip of their clip applier visible while operating. Methods We present a new dataset of 300 laparoscopic cholecystectomy videos with frame-wise annotation of clipper tip visibility. We further present ClipAssistNet, a neural network-based image classifier which detects the clipper tip visibility in single frames. ClipAssistNet ensembles predictions from 5 neural networks trained on different subsets of the dataset. Results Our model learns to classify the clipper tip visibility by detecting its presence in the image. Measured on a separate test set, ClipAssistNet classifies the clipper tip visibility with an AUROC of 0.9107, and 66.15% specificity at 95% sensitivity. Additionally, it can perform real-time inference (16 FPS) on an embedded computing board; this enables its deployment in operating room settings. Conclusion This work presents a new application of computer-assisted surgery for laparoscopic cholecystectomy, namely real-time feedback on adequate visibility of the clip applier. We believe this feedback can increase surgeons’ attentiveness when departing from safe visibility during the critical clipping of the cystic duct and artery.

scholarly journals PREDICTION AND MAPPING OF COCHLODINIUM POLYKRIKOIDES RED TIDE USING MACHINE LEARNING UNDER IMBALANCED DATA

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 143-145
Author(s):  
S. H. Bak ◽  
D. H. Hwang ◽  
U. Enkhjargal ◽  
H. J. Yoon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Red Tide ◽  
Imbalanced Data ◽  
Cochlodinium Polykrikoides ◽  
Model Learning ◽  
Data Set ◽  
Data Problem

Abstract. Cochlodinium polykrikoides (C. polykrikoides) is a phytoplankton that causes red tides every year in the middle of the South Sea of Korea. C. polykrikoides is a harmful Algae that has migratory ability and causes the fisheries damage over a long period of wide sea area if it causes red tide once. To minimize red tide damage, it is important to anticipate and prepare the red tide occurrence timing and location in advance. In this study, we predicted the occurrence of red tide of C. polykrikoides using machine learning techniques and compared the results of each algorithm. Logistic regression model, decision tree model, and multilayer neural network model were used for prediction of red tide occurrence. To produce the data set for model learning, we used the red tide occurrence map provided by the National Institute of Fisheries Science, the Local Data Assimilation and Prediction System (LDAPS) provided by the Korea Meteorological Agency, and the G1SST provided by the National Oceanic and Atmospheric Administration (NOAA). The feature vectors used for modeling consisted of 59 elements, which were made by using temperature, water temperature, precipitation, solar radiation, wind direction and wind speed. Only a very small number of red tide cases can be collected compared to the case of no red tide cases. Thus, an imbalance data problem arises in the data set. To overcome this imbalanced data problem, we used adding noise after oversampling to data of red tide occurrence to solve the difference of data between two classes.The data set is divided into 8 : 2 to prevent over-fitting and 80% is used as the learning data. The remaining 20% was used to evaluate the performance of each model. As a result of evaluating the prediction performance of each model, the multilayer neural network model showed the highest prediction accuracy.

scholarly journals Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels

2019 ◽  
Vol 6 (2) ◽  
pp. 224-232 ◽  
Author(s):  
Malinda Vania ◽  
Dawit Mureja ◽  
Deukhee Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Computer Assisted Surgery ◽  
Ct Images ◽  
Computer Assisted ◽  
Convolutional Network ◽  
Intervertebral Disks ◽  
Complex Anatomy ◽  
Global Consistency ◽  
Class Labels

Abstract There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study, we developed a fully automatic segmentation method to segment the spine from CT images, and we compared our segmentation results with reference segmentations obtained by well-known methods. We use a hybrid method. This method combines the convolutional neural network (CNN) and fully convolutional network (FCN), and utilizes class redundancy as a soft constraint to greatly improve the segmentation results. The proposed method was found to significantly enhance the accuracy of the segmentation results and the system processing time. Our comparison was based on 12 measurements: the Dice coefficient (94%), Jaccard index (93%), volumetric similarity (96%), sensitivity (97%), specificity (99%), precision (over segmentation 8.3 and under segmentation 2.6), accuracy (99%), Matthews correlation coefficient (0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and global consistency error (0.02). We experimented with CT images from 32 patients, and the experimental results demonstrated the efficiency of the proposed method. Highlights A method to enhance the accuracy of spine segmentation from CT data was proposed. The proposed method uses Convolutional Neural Network via redundant generation of class labels. Experiments show the segmentation accuracy has been enhanced.

Computer-Assisted Surgery in Craniomaxillofacial Surgery

Skull Base ◽  
2005 ◽  
Vol 15 (S 2) ◽  
Author(s):  
Ralf Gutwald ◽  
R. Schön ◽  
M. Metzger ◽  
C. Zizelmann ◽  
N.-C. Gellrich ◽  
...  
Keyword(s):  
Computer Assisted Surgery ◽  
Computer Assisted ◽  
Craniomaxillofacial Surgery

Accuracy and Practicability of Different Referencing Methods in Computer-Assisted Surgery of the Lateral Skull Base

Skull Base ◽  
2007 ◽  
Vol 16 (04) ◽  
Author(s):  
Klaus Stelter ◽  
Christoph Matthias ◽  
Kathrin Spiegl ◽  
Christian Lübbers ◽  
Andreas Leunig ◽  
...  
Keyword(s):  
Skull Base ◽  
Computer Assisted Surgery ◽  
Computer Assisted ◽  
Lateral Skull Base

Sonographically Aided Computer-Assisted Surgery at the Skull Base—Risks and Limitations

Skull Base ◽  
2007 ◽  
Vol 16 (04) ◽  
Author(s):  
Wolfgang Maier ◽  
Petra Lohnstein ◽  
Joerg Schipper
Keyword(s):  
Skull Base ◽  
Computer Assisted Surgery ◽  
Computer Assisted

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

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