IP Library › Granted Patent US 12,636,098
Granted Patent B2
US 12,636,098 · App. 18/028,130 · Granted May 26, 2026

Systems and methods for detecting, localizing, assessing, and visualizing bleeding in a surgical field

Inventors: Abhilash K. Pandya (Grosse Ile, MI); Mostafa Daneshgar Rahbar (Dearborn Heights, MI); Luke A. Reisner (Madison Heights, MI); Hao Ying (Novi, MI); Mahendra Bhandari (Novi, MI); Madhusudhan Reddiboina (Southfield, MI)
Assignees: Wayne State University; RediMinds, Inc.
A61B34/30A61B5/02042G06T7/0016A61B2090/365G06T2207/10016G06T2207/10024G06T2207/20081
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,636,098
App. No.
18/028,130
Filed
Mar 23, 2023
Granted
May 26, 2026
Kind
B2
Examiner
LIU, LI
Art Unit
2666
USPC
382/128
Abstract

Various systems, methods, and devices for identifying intraoperative bleeding are described. An example method includes identifying a first frame depicting a surgical scene; identifying a second frame depicting the surgical scene; identifying whether the second frame depicts bleeding by analyzing the first frame and the second frame; and outputting the second frame with an augmentation indicating whether bleeding is depicted in the second frame.

Claims (85)

1 . A robotic surgical system, comprising:

a camera configured to capture a video of a surgical scene;

an output device configured to display the video;

at least one processor; and

memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:

identifying a first frame in the video;

identifying a second frame in the video;

identifying that the second frame depicts bleeding by:

determining a ratio of low-entropy red pixels in the first frame, the low-entropy red pixels in the first frame comprising pixels in the first frame with entropy levels over a first threshold and red channel values over a second threshold;

determining a ratio of low-entropy red pixels in the second frame, the low-entropy red pixels in the second frame comprising pixels in the second frame with entropy levels over the first threshold and red channel values over the second threshold; and

determining that a difference between the ratio of low-entropy pixels in the second frame and the ratio of low-entropy red pixels in the second frame is greater than a third threshold; and

based on identifying that the second frame depicts the bleeding, causing the output device to display the second frame with an augmentation indicating the bleeding.

2 . The robotic surgical system of claim 1 , comprising:

a console configured to control surgical tools, the surgical tools comprising:

a scope comprising the camera;

the output device; and

an input device configured to receive an input from a user, the console controlling the surgical tools based on the input.

3 . The robotic surgical system of claim 1 , wherein determining the ratio of low-entropy red pixels in the first frame comprises:

generating a first entropy mask representing local entropies of the pixels in the first frame by convolving an entropy kernel with a first detection window of the first frame;

generating a first masked frame by performing pixel-by-pixel multiplication of the first entropy mask and the first frame; and

identifying a number of pixels in the first masked frame with red channel values over the second threshold, and

wherein determining the ratio of low-entropy red pixels in the second frame comprises:

generating a second entropy mask representing local entropies of the pixels in the second frame by convolving the entropy kernel with a second detection window of the second frame;

generating a second masked frame by performing pixel-by-pixel multiplication of the second entropy mask and the second frame; and

identifying a number of pixels in the second masked frame with red channel values over the second threshold.

4 . A method, comprising:

identifying a first frame depicting a surgical scene;

identifying a second frame depicting the surgical scene;

identifying whether the second frame depicts bleeding by analyzing the first frame and the second frame; and

outputting the second frame with an augmentation indicating whether bleeding is depicted in the second frame;

wherein identifying whether the second frame depicts bleeding comprises:

generating a first entropy mask representing local entropies of first pixels in the first frame;

generating a second entropy mask representing local entropies of second pixels in the second frame; and

determining whether the second frame depicts bleeding based on the first entropy mask and the second entropy mask.

5 . The method of claim 4 , wherein generating the first entropy mask comprises applying an entropy kernel to the first frame; and

wherein generating the second entropy mask comprises applying the entropy kernel to the second frame.

6 . The method of claim 5 , wherein generating the first entropy mask comprises:

calculating a first local entropy of a first pixel in the first frame by convolving a first detection window with the entropy kernel, the first frame comprising the first detection window, the first detection window comprising the first pixel;

generating a first entropy pixel by comparing the first local entropy to a first threshold, a first value of the first entropy pixel being a first level or a second level based on whether the first local entropy is less than the first threshold; and

generating the first entropy mask to include the first entropy pixel, and wherein generating the second entropy mask comprises:

calculating a second local entropy of a second pixel in the second frame by convolving a second detection window with the entropy kernel, the second frame comprising the second detection window, the second detection window comprising the second pixel;

generating a second entropy pixel by comparing the second local entropy to the first threshold, a second value of the first entropy pixel being the first level or the second level based on whether the second local entropy is less than the first threshold; and

generating the second entropy mask to include the second entropy pixel.

7 . The method of claim 5 , wherein applying the entropy kernel to the first frame comprises convolving the entropy kernel with a first detection window of the first frame, and

wherein applying the entropy kernel to the second frame comprises convolving the entropy kernel with a second detection window of the second frame.

8 . The method of claim 4 , wherein determining whether the second frame depicts bleeding based on the first entropy mask and the second entropy mask comprises:

generating a first masked frame by performing pixel-by-pixel multiplication of the first entropy mask and the first frame;

identifying a first number of red pixels in the first masked frame;

generating a second masked frame by performing pixel-by-pixel multiplication of the second entropy mask and the second frame;

identifying a second number of red pixels in the second masked frame; and

determining whether the second frame depicts bleeding by comparing the first number and the second number.

9 . The method of claim 4 , wherein the first frame and the second frame are identified in multiple frames of a video, and

wherein the first frame and the second frame are nonconsecutive frames in the video.

10 . The method of claim 4 , wherein identifying whether the second frame depicts bleeding comprises determining that the second frame depicts the bleeding.

11 . The method of claim 10 , wherein outputting the second frame with the augmentation comprises:

identifying a portion of the first frame depicting a physiological structure obscured by the bleeding in the second frame, the first frame depicting the physiological structure without the bleeding; and

outputting the augmentation as a visual overlay of the second frame, the augmentation comprising the portion of the first frame.

12 . The method of claim 10 , further comprising:

determining a location of a source of the bleeding by:

identifying a region of the second frame depicting red pixels corresponding to less than a first threshold of local entropies, the region comprising a cluster of the red pixels; and

determining that the location of the source of the bleeding is within the region.

13 . The method of claim 10 , further comprising:

determining a location of a source of the bleeding, wherein determining the location of the source of the bleeding comprises determining a centroid of a region, the region being a largest cluster of red pixels corresponding to less than a first threshold of local entropies in the second frame.

14 . The method of claim 10 , further comprising:

determining a magnitude of the bleeding by:

identifying a region of the second frame depicting red pixels corresponding to lower than a first threshold of local entropies and red values greater than a threshold red value, the region comprising a cluster of the red pixels; and

determining the magnitude of the bleeding based on a change in an area of the region of the second frame and a corresponding area of a frame subsequent to the second frame.

15 . A robotic surgical system, comprising:

a tool comprising at least one sensor configured to generate a feedback signal indicating that the tool has touched a physiological structure;

at least one processor; and

memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:

identifying a first frame depicting a surgical scene;

identifying a second frame depicting the surgical scene;

identifying whether the second frame depicts bleeding by analyzing the first frame and the second frame and further based on the feedback signal; and

outputting the second frame with an augmentation indicating whether bleeding is depicted in the second frame.

16 . The robotic surgical system of claim 15 , further comprising:

a scope configured to obtain the first frame and the second frame; and/or

a display configured to output the first frame and the second frame.

17 . The robotic surgical system of claim 16 , further comprising: a tool comprising a 3-dimensional (3D) scanner,

wherein the operations further comprise: receiving, from the 3D scanner, volumetric data depicting the surgical scene.

18 . The robotic surgical system of claim 16 , further comprising: a tool comprising a camera configured to capture the first frame and the second frame,

wherein the operations further comprise: causing the tool to reposition based on whether the second frame depicts bleeding.

19 . The robotic surgical system of claim 16 , further comprising: one or more tools configured to stop the bleeding,

wherein the operations further comprise causing the one or more tools to stop the bleeding in the surgical scene.

20 . The robotic surgical system of claim 15 , further comprising a console configured to control one or more surgical tools, the one or more surgical tools comprising a scope configured to obtain the first frame and the second frame.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 31, 2023
From: PANDYA, ABHILASH K.; RAHBAR, MOSTAFA DANESHGAR; REISNER, LUKE A.; YING, HAO
To: WAYNE STATE UNIVERSITY
Reel/Frame 063817/0815 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 31, 2023
From: BHANDARI, MAHENDRA; REDDIBOINA, MADHU
To: REDIMINDS, INC.
Reel/Frame 063817/0834 →
Continuity (2)
Provisional Application 63082459 · Sep 23, 2020
Related Publication 20230363836A1 · Nov 16, 2023
References Cited (49)
US 10758309B1 · Chow · 2020 [cited by examiner]
US 20070230930A1 · Chang · 2007 [cited by applicant]
US 20120138658A1 · Ullrich et al. · 2012 [cited by applicant]
US 20120157820A1 · Zhang et al. · 2012 [cited by applicant]
US 20120316421A1 · Kumar · 2012 [cited by examiner]
US 20180125333A1 · Zhao et al. · 2018 [cited by applicant]
US 20180247560A1 · Mackenzie · 2018 [cited by examiner]
US 20190110856A1 · Barral · 2019 [cited by examiner]
US 20200265273A1 · Wei et al. · 2020 [cited by applicant]
US 20210142487A1 · Xu · 2021 [cited by examiner]
US 20210216822A1 · Paik · 2021 [cited by examiner]
US 20210321887A1 · Fukazawa · 2021 [cited by examiner]
International Search Report and Written Opinion for PCT Application No. PCT/US2021/051590, mailed Dec. 10, 2021, 13 pages. [cited by applicant]
Boulougoura, et al., “Intelligent Systems For Computer-Assisted Clinical Endoscopic Image Analysis,” Proceedings of the second IASTED international conference on biomedical engineering, Acta Press, 2004, pp. 405-408. [cited by applicant]
Castillo, et al., “Laparoscopic Repair of an Iliac Artery Injury During Radical Cystoprostatectomy,” Surgical Laparoscopy, Endoscopy & Percutaneous Techniques, vol. 18, No. 3, 2008, pp. 315-318. [cited by applicant]
Dabass, et al., “Mammogram Image Enhancement Using Entropy and CLAHE Based Intuitionistic Fuzzy Method,” 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019, pp. 24-29. [cited by applicant]
Fang and Zhao, “A hybrid active contour model based on global and local information for medical image segmentation,” Multidimensional Systems and Signal Processing, vol. 30, 2019, pp. 689-703. [cited by applicant]
Fu, “Computer-Aided Bleeding Detection in WCE Video,” IEEE Journal of Biomedical and Health Informatics, vol. 18, No. 2, 2014, pp. 636-642. [cited by applicant]
Garisto, et al., “Robot-Assisted Single Port Partial Nephrectomy Using The Sp Surgical System: First Clinical Experience,” The Journal of Urology, vol. 201, No. 4S, 2019, pp. e848. [cited by applicant]
German, et al., “Entropy-based image merging,” Proceedings of the Second Canadian Conference on Computer and Robot Vision (CRV'05), 2005, pp. 81-86. [cited by applicant]
Hwang, et al., “Blood Detection in Wireless Capsule Endoscopy using Expectation Maximization Clustering,” Proc. of SPIE, vol. 6144, 2006, pp. 1-11. [cited by applicant]
Ishihara, et al., “Infrared endoscopic system for bleeding-point detection after flushing with indocyanine green solution,” Gastrointestinal Endoscopy, vol. 68, No. 5, 2008, pp. 975-981. [cited by applicant]
Jung, et al., “Active Blood Detection in a High Resolution Capsule Endoscopy using Color Spectrum Transformation,” 2008 International Conference on BioMedical Engineering and Informatics, 2008, pp. 859-862. [cited by applicant]
Lau and Correia, “Detection of bleeding patterns in WCE video using multiple features,” Proceedings of the 29th Annual International Conference of the IEEE EMBS, 2007, pp. 5601-5604. [cited by applicant]
Li and Meng, “Computer Aided Detection Of Bleeding In Capsule Endoscopy Images,” 2008 Canadian Conference on Electrical and Computer Engineering, 2008, pp. 001963-001966. [cited by applicant]
Li and Meng, “Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images,” IEEE Transactions on Biomedical Engineering, vol. 56, No. 4, 2009, pp. 1032-1039. [cited by applicant]
Liu and Yuan, “Obscure bleeding detection in endoscopy images using support vector machines,” Optimization and Engineering, vol. 10, 2009, pp. 289-299. [cited by applicant]
Mackiewicz, et al., “Bleeding Detection in Wireless Capsule Endoscopy using adaptive colour histogram model and Support Vector Classification,” Medical Imaging 2008: Image Processing, Proc. of SPIE, vol. 6914, 2008, pp.… [cited by applicant]
Makihara, et al., “Object Recognition Supported by User Interaction for Service Robots,” 2002 International Conference on Pattern Recognition, vol. 3, 2002, pp. 561-564. [cited by applicant]
Pan, et al., “Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network,” Journal of Medical Systems, vol. 35, 2011, pp. 1477-1484. [cited by applicant]
Pan, et al., “BP neural network classification for bleeding detection in wireless capsule endoscopy,” Journal of Medical Engineering & Technology, vol. 33, No. 7, 2009, pp. 575-581. [cited by applicant]
Penna, et al., “A Technique For Blood Detection In Wireless Capsule Endoscopy Images,” 17th European Signal Processing Conference, 2009, pp. 1864-1868. [cited by applicant]
Rahbar, et al., “An entropy-based approach to detect and localize intraoperative bleeding during minimally invasive surgery”, vol. 16, No. 6, 2020, pp. 1-9. [cited by applicant]
Schäfer, et al., “A Nation's Experience of Bleeding Complications during Laparoscopy,” The American Journal of Surgery, vol. 180, No. 1, 2000, pp. 73-77. [cited by applicant]
Skilling and Bryan, “Maximum entropy image reconstruction: general algorithm,” Royal Astronomical Society, vol. 211, No. 1, 1984, pp. 111-124. [cited by applicant]
Search Report for European Application No. 21873369.9, Dated Sep. 20, 2024, 10 pages. [cited by applicant]
Talab, et al. “Management Of Intraoperative Complications During Robotic Renal And Ureteral Oncologic Surgery: The Lessons We Learned,” The Journal Of Urology, vol. 201, No. 4S, 2019, pp. e851. [cited by applicant]
Wu, et al., “Local Shannon entropy measure with statistical tests for image randomness,” Information Sciences, vol. 222, 2013, pp. 323-342. [cited by applicant]
Yan, et al., “Local entropy-based transition region extraction and thresholding,” Pattern Recognition Letters, vol. 24, No. 16, 2003, pp. 2935-2941. [cited by applicant]
Zhang, et al., “Prevention and management of hemorrhage during a laparoscopic colorectal surgery,” Annals of Laparoscopic and Endoscopic Surgery, vol. 1, No. 40, 2016, pp. 1-6. [cited by applicant]
Zong, et al., “Automatic ultrasound image segmentation based on local entropy and active contour model,” Computers and Mathematics with Applications, vol. 78, 2019, pp. 929-943. [cited by applicant]
Al-Rahayfeh, et al., “Detection of Bleeding in Wireless Capsule Endoscopy Images Using Range Ration Color,” The International Journal of Multimedia & Its Applications, vol. 2, No. 2, 2010, pp. 1-10. [cited by applicant]
Barros, “Vascular injuries during gynecological laparoscopy—the vascular surgeon's advice,” Sao Paulo Med J., vol. 123, No. 1, 2005, pp. 38-41. [cited by applicant]
Bourbakis, et al., “A Neural Network-based Detection of Bleeding in sequences of WCE images,” Proceedings of the 5th IEEE Symposium on Bioinformatics and Bioengineering, 2005, 4 pages. [cited by applicant]
Jaiswal, et al., “Morphological Method, PCA and LDA With Neural Networksface Recognition,” Journal of Global Research in Computer Science, vol. 2, No. 6, 2011, pp. 35-48. [cited by applicant]
Kaushik, “Bleeding complications in laparoscopic cholecystectomy: Incidence, mechanisms, prevention and management,” J Minim Access Surg., vol. 6, No. 3, 2010, pp. 59-65. [cited by applicant]
Khosravi, “A Pixon-based Image Segmentation Method Considering Textural Characteristics of Image,” Journal of AI and Data Mining, vol. 7, No. 1, 2019, pp. 27-34. [cited by applicant]
Michalak & Okarma, “Improvement of Image Binarization Methods Using Image Preprocessing with Local Entropy Filtering for Alphanumerical Character Recognition Purposes,” Entropy, vol. 21, No. 562, 2019, pp. 1-18. [cited by applicant]
Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, No. 1, 1979, pp. 62-66. [cited by applicant]