Mostafa Daneshgar Rahbar

Dr. Mostafa Daneshgar Rahbar

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Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 December 2020 in The International Journal of Medical Robotics and Computer Assisted Surgery
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Journal Article
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MRMostafa Daneshgar Rahbar
Mostafa Daneshgar Rahbar
LRLuke Reisner
Luke Reisner
HYHao Ying
Hao Ying
Abhilash PandyaAbhilash Pandya
Abhilash Pandya
21 February 2021 in Robotics
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MRMostafa Daneshgar Rahbar
Mostafa Daneshgar Rahbar
Hao YingHao Ying
Hao Ying
Abhilash PandyaAbhilash Pandya
Abhilash Pandya
Unintentional vascular damage can result from a surgical instrument’s abrupt movements during minimally invasive surgery (laparoscopic or robotic). A novel real-time image processing algorithm based on local entropy is proposed that can detect abrupt movements of surgical instruments and predict bleeding occurrence. The uniform nature of the texture of surgical tools is utilized to segment the tools from the background. By comparing changes in entropy over time, the algorithm determines when the surgical instruments are moved abruptly. We tested the algorithm using 17 videos of minimally invasive surgery, 11 of which had tool-induced bleeding. Our preliminary testing shows that the algorithm is 88% accurate and 90% precise in predicting bleeding. The average advance warning time for the 11 videos is 0.662 s, with the standard deviation being 0.427 s. The proposed approach has the potential to eventually lead to a surgical early warning system or even proactively attenuate tool movement (for robotic surgery) to avoid dangerous surgical outcomes.
29 May 2024 in Healthcare
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MRMostafa Daneshgar Rahbar
Mostafa Daneshgar Rahbar
GPGeorge Pappas
George Pappas
NJNabih Jaber
Nabih Jaber
Background: Open surgery relies heavily on the surgeon’s visual acuity and spatial awareness to track instruments within a dynamic and often cluttered surgical field. Methods: This system utilizes a head-mounted depth camera to monitor surgical scenes, providing both image data and depth information. The video captured from this camera is scaled down, compressed using MPEG, and transmitted to a high-performance workstation via the RTSP (Real-Time Streaming Protocol), a reliable protocol designed for real-time media transmission. To segment surgical instruments, we utilize the enhanced U-Net with GridMask (EUGNet) for its proven effectiveness in surgical tool segmentation. Results: For rigorous validation, the system’s performance reliability and accuracy are evaluated using prerecorded RGB-D surgical videos. This work demonstrates the potential of this system to improve situational awareness, surgical efficiency, and generate data-driven insights within the operating room. In a simulated surgical environment, the system achieves a high accuracy of 85.5% in identifying and segmenting surgical instruments. Furthermore, the wireless video transmission proves reliable with a latency of 200 ms, suitable for real-time processing. Conclusions: These findings represent a promising step towards the development of assistive technologies with the potential to significantly enhance surgical practice.
18 December 2023 in Journal of Imaging
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MRMostafa Daneshgar Rahbar
Mostafa Daneshgar Rahbar
SMSeyed Ziae Mousavi Mojab
Seyed Ziae Mousavi Mojab
This study proposed enhanced U-Net with GridMask (EUGNet) image augmentation techniques focused on pixel manipulation, emphasizing GridMask augmentation. This study introduces EUGNet, which incorporates GridMask augmentation to address U-Net’s limitations. EUGNet features a deep contextual encoder, residual connections, class-balancing loss, adaptive feature fusion, GridMask augmentation module, efficient implementation, and multi-modal fusion. These innovations enhance segmentation accuracy and robustness, making it well-suited for medical image analysis. The GridMask algorithm is detailed, demonstrating its distinct approach to pixel elimination, enhancing model adaptability to occlusions and local features. A comprehensive dataset of robotic surgical scenarios and instruments is used for evaluation, showcasing the framework’s robustness. Specifically, there are improvements of 1.6 percentage points in balanced accuracy for the foreground, 1.7 points in intersection over union (IoU), and 1.7 points in mean Dice similarity coefficient (DSC). These improvements are highly significant and have a substantial impact on inference speed. The inference speed, which is a critical factor in real-time applications, has seen a noteworthy reduction. It decreased from 0.163 milliseconds for the U-Net without GridMask to 0.097 milliseconds for the U-Net with GridMask.
29 October 2025
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Book Chapter
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DKDaulet Kaldybek
Daulet Kaldybek
MRMostafa Daneshgar Rahbar
Mostafa Daneshgar Rahbar
NJNabih Jaber
Nabih Jaber
7 May 2024 in Preprints.org
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Preprint
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MRMostafa Daneshgar Rahbar
Mostafa Daneshgar Rahbar
GPGeorge Pappas
George Pappas
NJNabih Jaber
Nabih Jaber
A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.