Deep-learning based automated instrument tracking and adaptive-sampling of intraoperative OCT for video-rate volumetric imaging of ophthalmic surgical maneuvers

2019 ◽  
Author(s):  
Mohamed T. El-Haddad ◽  
Joseph D. Malone ◽  
Nancy T. Hoang ◽  
Yuankai K. Tao
Keyword(s):  
Deep Learning ◽  
Adaptive Sampling ◽  
Volumetric Imaging ◽  
Instrument Tracking ◽  
Automated Instrument ◽  
Intraoperative Oct

Deep-learning on-chip light-sheet microscopy enabling video-rate volumetric imaging of dynamic biological specimens

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Xiaopeng Chen ◽  
Junyu Ping ◽  
Yixuan Sun ◽  
Chengqiang Yi ◽  
Sijian Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Scattering ◽  
Light Sheet ◽  
High Contrast ◽  
Spatiotemporal Resolution ◽  
Volumetric Imaging ◽  
Light Sheet Microscopy ◽  
High Spatiotemporal Resolution ◽  
On Chip

Volumetric imaging of dynamic signals in a large, moving, and light-scattering specimen is extremely challenging, owing to the requirement on high spatiotemporal resolution and difficulty in obtaining high-contrast signals. Here...

scholarly journals Review and Prospect: Artificial Intelligence in Advanced Medical Imaging

2021 ◽  
Vol 1 ◽  
Author(s):  
Shanshan Wang ◽  
Guohua Cao ◽  
Yan Wang ◽  
Shu Liao ◽  
Qian Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Volumetric Imaging ◽  
Positron Emission ◽  
Potential Applications ◽  
Magnetic Resonance Imaging Mri

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.

scholarly journals Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens

2020 ◽  
Vol 11 (12) ◽  
pp. 7273
Author(s):  
Fang Zhao ◽  
Lanxin Zhu ◽  
Chunyu Fang ◽  
Tingting Yu ◽  
Dan Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Light Sheet ◽  
Volumetric Imaging

Deep Learning Assisted Robotic Magnetic Anchored and Guided Endoscope for Real-Time Instrument Tracking

2021 ◽  
pp. 1-1
Author(s):  
Truman Cheng ◽  
Weibing Li ◽  
Wing Yin Ng ◽  
Yisen Huang ◽  
Jixiu Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Instrument Tracking

Automated real-time instrument tracking for microscope-integrated intraoperative OCT imaging of ophthalmic surgical maneuvers

2015 ◽  
Author(s):  
Mohamed T. El-Haddad ◽  
Justis P. Ehlers ◽  
Sunil K. Srivastava ◽  
Yuankai K. Tao
Keyword(s):  
Real Time ◽  
Instrument Tracking ◽  
Oct Imaging ◽  
Intraoperative Oct

scholarly journals Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based Multiple Surgical Instrument Tracking in Training and Actual Operations

2020 ◽  
Vol 9 (6) ◽  
pp. 1964
Author(s):  
Dongheon Lee ◽  
Hyeong Won Yu ◽  
Hyungju Kwon ◽  
Hyoun-Joong Kong ◽  
Kyu Eun Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Robotic Surgery ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Surgical Skills ◽  
Surgical Instruments ◽  
Skill Assessment ◽  
Surgical Instrument ◽  
Surgical Skill ◽  
Instrument Tracking

As the number of robotic surgery procedures has increased, so has the importance of evaluating surgical skills in these techniques. It is difficult, however, to automatically and quantitatively evaluate surgical skills during robotic surgery, as these skills are primarily associated with the movement of surgical instruments. This study proposes a deep learning-based surgical instrument tracking algorithm to evaluate surgeons’ skills in performing procedures by robotic surgery. This method overcame two main drawbacks: occlusion and maintenance of the identity of the surgical instruments. In addition, surgical skill prediction models were developed using motion metrics calculated from the motion of the instruments. The tracking method was applied to 54 video segments and evaluated by root mean squared error (RMSE), area under the curve (AUC), and Pearson correlation analysis. The RMSE was 3.52 mm, the AUC of 1 mm, 2 mm, and 5 mm were 0.7, 0.78, and 0.86, respectively, and Pearson’s correlation coefficients were 0.9 on the x-axis and 0.87 on the y-axis. The surgical skill prediction models showed an accuracy of 83% with Objective Structured Assessment of Technical Skill (OSATS) and Global Evaluative Assessment of Robotic Surgery (GEARS). The proposed method was able to track instruments during robotic surgery, suggesting that the current method of surgical skill assessment by surgeons can be replaced by the proposed automatic and quantitative evaluation method.

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