Research Overview

I have done research in a number of divers areas:

Ultrasound Image Enhancement and Machine Learning:

I have developed a broad set of computational tools for improving the temporal information generated from 3D imaging, specifically creating high frame rate sequences and de-noised volume images of the mitral and aortic valves from 3D echo. The work boosted Ultrasound frame rater from a native 25 volumes a second to over 500 across a single cardiac cycle. This high frame rate ultrasound is very useful for analyzing the dynamics of the heart valve leaflets during their closure, something previously impossible to image. Such high frame rates provide “small motion assumption” necessary to use many computer vision techniques for segmentation and tracking of valvular structures during the heartbeat. These, in turn, provide a novel tool with which to study valve dynamics beyond the traditional Doppler echo views. This work, though the small motion assumption, enables is volume registration. This, in turn, provides a means of stabilizing the images to enhance the clinical view. I have also published work on the application of machine learning (deep neural networks) to the task of detecting and classifying congenital heart diseases in neonates. It is exciting to see how effective these powerful methodologies can be when coupled with our vast collections of clinical data.

My work in extending the dynamic range of ultrasound images using high dynamic range processing algorithms given the best paper award when presented. Using this method ultrasound machines can simultaneously image highly echogenic (bone) and low echogenic (tissues and deep structures) providing a more informative and holistic view to the clinician.

Image Processing and Computer vision:

My PhD thesis work in computer vision and robotics extended energy minimizing active deformable models (a computer vision approach) into the real-time domain for the first time and applied the results directly to robot control. I applied these efficient deformable models to a number of problems from pedestrian and mobile robot tracking to visual servo control of robotic manipulators allowing grasping of previously unknown, by the robot, objects. I applied these methods to the segmentation of 3D real-time ultrasound imagery for minimally invasive intracardiac procedures. In addition to real-time implementation, I have proven theoretical bounds on the accuracy and convergence of my methods with respect to mathematical, geometric definition of smoothness. I showed the efficiency of the second derivative of curvature minimization as a regularizing term for deformable models instead of using curvature directly as was done in prior work.

Surgical and Imaging Tools for Image-Guided Cardiac Interventions:

Image-guided medical procedures are an ideal application domain for surgical robotics research and are a logical extension my work with manipulators, and computer vision guided control. While receiving NIH funding from two consecutive BRP grants I have leveraged my experience to developed clinically useful systems to enabling technology to achieve beating heart repair of congenital heart defects.

User Interfaces:

My first work in user interfaces was quantifying the interface design process for a system used to view confocal microscopy images of rat neurobiology. These large data sets were shared across two sites: one in the US and one in Sweden. One of the problems I advanced was how to build a user interface for simultaneous viewing and manipulation of enormous data sets across large geographic (latency) distances. This work received the best paper and best student paper awards when presented. I have also studied how the sense of touch (haptics) can be electro-mechanically transmitted as part of an interface to surgical instruments and simulators. The transmission of haptic information from the patient to the surgeon through a user interface provides a sense of touch previously unavailable in minimally invasive/robotic procedures. I have demonstrated the use of these haptic systems for remote palpation by integrated an existing haptic display with a manipulator and conducted a user study examining stiffness discrimination ability with tactile and kinesthetic (classic) force feedback components.

About Dr. Douglas Perrin

I have over 13 years of experience in translating research advances in computer science into medicine. For many people, computers are just passive boxes that sit on desks. However, for me, the machine is an active part of the world around it. It observes the environment and acts based on its observations and the intentions of users. I develop systems that are not mere tools but are interactive parts of their environment that extend human capabilities. Interfaces must be established between the artificial system and its human collaborator to harness the potential of these systems. Toward that end, my research career has spanned the areas of computer vision, image analysis, robotics, artificial intelligence (specifically machine learning), and user interfaces.

My long-term goal has been to explore the use of computing and robotics to improve clinical applications and ultimately benefit patients. Computer-aided medicine has the potential to change the face of medicine by enhancing clinical skills, expediting procedures, and providing novel diagnostics tools. Unfortunately, over the years I have seen implementation lag far behind the science of computing and its underlying mathematics. For my part, I have tried to address this through collaboration with clinicians to identify areas that could benefit from improved user interfaces, robotics instrumentation, and qualitative information improvements derived from image processing and analysis, machine learning and mathematical modeling.

I use my expertise to support the development of enhancements to cardiac imaging at Boston Children’s Hospital. The long-term goal is that the imaging enchantments and analysis methods I provide will have a positive impact on the diagnosis, preoperative planning, and intervention (through real-time methods) of congenital heart disease. I have found the interdisciplinary nature of computer science and its application to medicine provides a fertile field of potential research and I look forward to continuing exploring the potential that computers offer to advance medicine.

Researcher Services

Researcher Areas

  • Artificial Intelligence
  • Cluster Computing
  • Compressed sensing and other L1 optimization techniques
  • Computer Science
  • Congenital Heart Defects
  • Machine Learning
  • Medical Devices
  • Medical imaging
  • Pediatric Heart Surgery
  • Robotics
  • Ultrasound

Research Departments

PUBLICATIONS

Publications powered by Harvard Catalyst Profiles

  1. Degirmenci A, Perrin DP, Howe RD. High dynamic range ultrasound imaging. Int J Comput Assist Radiol Surg. 2018 May; 13(5):721-729. View abstract
  2. Villard PF, Hammer PE, Perrin DP, Del Nido PJ, Howe RD. Fast image-based mitral valve simulation from individualized geometry. Int J Med Robot. 2018 Apr; 14(2). View abstract
  3. Ghelani SJ, Brown DW, Kuebler JD, Perrin D, Shakti D, Williams DN, Marx GR, Colan SD, Geva T, Harrild DM. Left Atrial Volumes and Strain in Healthy Children Measured by Three-Dimensional Echocardiography: Normal Values and Maturational Changes. J Am Soc Echocardiogr. 2018 Feb; 31(2):187-193.e1. View abstract
  4. Feins EN, Lee Y, O'Cearbhaill ED, Vasilyev NV, Shimada S, Friehs I, Perrin D, Hammer PE, Yamauchi H, Marx G, Gosline A, Arabagi V, Karp JM, Del Nido PJ. A Growth-Accommodating Implant for Paediatric Applications. Nat Biomed Eng. 2017; 1:818-825. View abstract
  5. Pereira F, Bueno A, Rodriguez A, Perrin D, Marx G, Cardinale M, Salgo I, Del Nido P. Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms. J Med Imaging (Bellingham). 2017 Jan; 4(1):014502. View abstract
  6. Franklin Pereira ; Alejandra Bueno ; Andrea Rodriguez ; Douglas Perrin ; Gerald Marx ; Michael Cardinale ; Ivan Salgo ; Pedro del Nido. Journal of Medical Imaging. Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms . 2017; 1(4):014502. View abstract
  7. A. B. Terentjev, S. H. Settlemier, D. P. Perrin, P. J. del Nido, I. V. Shturts, and N. V. Vasilyev. SPIE Medical Imaging. Temporal enhancement of two-dimensional color doppler echocardiography. 2016; 9784. View abstract
  8. Ghelani, S.J., Perrin, D.P., Brown, D.W., Shakti, D., Williams, D., Marx, G.R., Colan, S.D., Geva, T. and Harrild, D.M. three-dimensional Echocardiography-based Assessment of Left Atrial Size and Deformation in Healthy Children: p2-179. Journal of the American Society of Echocardiography. 2015; 6(28):B124. View abstract
  9. Samuel Kesner, Robert Howe, Shelton Yuen, Pedro Del Nido, Douglas Perrin, Nikolay Vasilyev. Motion compensating catheter device. 2013. View abstract
  10. Perrin DP, Vasilyev NV, Marx GR, del Nido PJ. Temporal enhancement of 3D echocardiography by frame reordering. JACC Cardiovasc Imaging. 2012 Mar; 5(3):300-4. View abstract
  11. Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, del Nido PJ, Howe RD. Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow. Med Image Anal. 2012 Feb; 16(2):497-504. View abstract
  12. Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, Del Nido PJ, Howe RD. Real-time image-based rigid registration of three-dimensional ultrasound. Med Image Anal. 2012 Feb; 16(2):402-14. View abstract
  13. Kaza E, Marx GR, Kaza AK, Colan SD, Loyola H, Perrin DP, Del Nido PJ. Changes in left atrioventricular valve geometry after surgical repair of complete atrioventricular canal. J Thorac Cardiovasc Surg. 2012 May; 143(5):1117-24. View abstract
  14. Schneider RJ, Tenenholtz NA, Perrin DP, Marx GR, del Nido PJ, Howe RD. Patient-specific mitral leaflet segmentation from 4D ultrasound. Med Image Comput Comput Assist Interv. 2011; 14(Pt 3):520-7. View abstract
  15. Yuen SG, Perrin DP, Vasilyev NV, Del Nido PJ, Howe RD. Force Tracking with Feed-Forward Motion Estimation for Beating Heart Surgery. IEEE Trans Robot. 2010 Aug 16; 26(5):888-896. View abstract
  16. Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, del Nido PJ, Howe RD. Mitral annulus segmentation from 3D ultrasound using graph cuts. IEEE Trans Med Imaging. 2010 Sep; 29(9):1676-87. View abstract
  17. Kawata M, Vasilyev NV, Perrin DP, del Nido PJ. Beating-heart mitral valve suture annuloplasty under real-time three-dimensional echocardiography guidance: an ex vivo study. Interact Cardiovasc Thorac Surg. 2010 Jul; 11(1):6-9. View abstract
  18. Yuen SG, Yip MC, Vasilyev NV, Perrin DP, Del Nido PJ, Howe RD. Robotic Force Stabilization for Beating Heart Intracardiac Surgery. Med Image Comput Comput Assist Interv. 2009 Oct 01; 5761(2009):26-33. View abstract
  19. Perrin DP, Vasilyev NV, Novotny P, Stoll J, Howe RD, Dupont PE, Salgo IS, del Nido PJ. Image guided surgical interventions. Curr Probl Surg. 2009 Sep; 46(9):730-66. View abstract
  20. Perrin DP, Vasilyev NV, Novotny P, Stoll J, Howe RD, Dupont PE, Salgo IS, Del Nido PJ. In brief. Curr Probl Surg. 2009 Sep; 46(9):723-7. View abstract
  21. Yuen SG, Yip MC, Vasilyev NV, Perrin DP, del Nido PJ, Howe RD. Robotic force stabilization for beating heart intracardiac surgery. Med Image Comput Comput Assist Interv. 2009; 12(Pt 1):26-33. View abstract
  22. Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, Del Nido PJ, Howe RD. Mitral Annulus Segmentation from Three-Dimensional Ultrasound. Proc IEEE Int Symp Biomed Imaging. 2009; 779-782. View abstract
  23. Hammer, P.E., Vasilyev, N.V., Perrin, D.P., Del Nido, P.J. Fast image-based model of mitral valve closure for surgical planning. Computational Biomechanics for Medicine. 2008; 1(1):15-26. View abstract
  24. Wagner, C.R. and Perrin, D.P. Efficient curvature estimations for real-time (25Hz) segmentation of volumetric ultrasound data. Medical Imaging, International Society for Optics and Photonics. 2008; 6914(1):69144H. View abstract
  25. Hammer, P.E., Perrin, D.P., Pedro, J. and Howe, R.D. Image-based mass-spring model of mitral valve closure for surgical planning. Medical Imaging: Visualization, Image-Guided Procedures, and Modeling. 2008; 6918(1):69180Q. View abstract
  26. Douglas P Perrin, Robert D Howe. Actuated tether. 2007. View abstract
  27. Christopher R Wagner, Douglas P Perrin, Robert D Howe, Nikolay Vasilyev, Pedro J del Nido. Force feedback in a three-dimensional ultrasound-guided surgical task. Haptic Interfaces for Virtual Environment and Teleoperator Systems. 2006; 1(14):43-48. View abstract
  28. Christopher R Wagner, Douglas P Perrin, Ross L Feller, Robert D Howe, Olivier Clatz, Hervé Delingette, Nicholas Ayache. Integrating tactile and force feedback with finite element models. Robotics and Automation. 2005; 1(1):3942-3947. View abstract
  29. The effect of force feedback on remote palpation. The effect of force feedback on remote palpation. Robotics and Automation. 2004; 1(1):782-788. View abstract
  30. Douglas P Perrin, Esra Kadioglu, Sascha A Stoeter, Nikolaos Papanikolopoulos. Grasping and tracking using constant curvature dynamic contours. International Journal of Robotics Research. 2003; 22(10-11):855-871. View abstract
  31. Douglas Paul Perrin. Vision-based tasks and dynamic contours. 2002. View abstract
  32. Douglas P Perrin, Christopher E Smith. Rethinking classical internal forces for active contour models. Computer Vision and Pattern Recognition. 2001; 2(1):11. View abstract
  33. Christopher E Smith, Doug Perrin. HOLDeR: a layered system for vision-guided robotics. Systems, Man, and Cybernetics. 2000; 2(1):1454-1459. View abstract
  34. I Pavlidis, D Perrin, NP Papanikolopoulos, W Au, S Sawtelle. A ground truth tool for Synthetic Aperture Radar (SAR) imagery. Computer Vision Beyond the Visible Spectrum: Methods and Applications. 1999; 1(1):82-87. View abstract
  35. Alex Safonov, Douglas Perrin, Joseph A Konstan, John V Carlis, Robert Elde. Lessons from the Neighborhood Viewer: Building Innovative Collaborative Applications in Tcl and Tk. Usenix: Tcl/Tk Workshop. 1996; 1(1):1-11. View abstract