ESI Program Curriculum

Students who pursue this degree commonly hold a bachelor’s degree in a conventional engineering discipline (e.g., mechanical, electrical, or biomedical engineering) or computer science. However, the program is also adaptable to other STEM areas as well (e.g., neuroscience, physics, mathematics, etc.). If you have questions, be sure to send inquiries to vuse.esi@vanderbilt.edu.

With regard to program structure, the master of engineering in surgery and intervention has two tracks: the innovator track and the inventor track. Each track is 30 credit hours and consists of core course work and electives — learn more about these two tracks below:

Innovator Track

The innovator track program is a one-year⁺ program specifically structured to enable students who are constrained by career path developments such that extended multi-year study is not possible. The goals of the program are to quickly provide enhanced skill sets with rigorous study, as well as provide important exposure to many clinical domains. This track sequence is Fall Semester - Spring Semester - Summer Session I - Summer Session II.

Typical curriculum for the innovator track:

Inventor Track

The inventor track program is the same 30 credit hour program but is a more conventional option consisting of two-years of courses. This track is specifically structured to enable students who have recently graduated with a bachelor’s degree and who wish to spend a concentrated period of time gaining skill sets within the engineering and surgery/intervention domain. This extended structure allows students to spend additional time within the novel research/design VISE environment to assist in the inception phase of their platform technologies in surgery and intervention. This track sequence is Fall Semester I - Spring Semester 1 - Fall Semester II - Spring Semester II. 

Typical curriculum for the inventor track:

Course Descriptions

Within the master of engineering in surgery and intervention, there are five required core courses: one immersion, one methods, one professional, and two design. The remainder of the degree involves an additional five electives

⁺ Substitution of core components may be possible in consultation with the Program Director.

⁺⁺For the 2023-2024 academic year, students needing to fulfill the Professional Development course requirements, enrollment in ENGM 6500 is required.

ESI – Immersion:

• BME 6301 — Engineering in Surgery and Intervention: Provocative Questions 

• This course is designed to provide an in depth clinical immersion with a scaffold design involving ten or more physicians from a variety of medical specialties discussing disease and dysfunction background, and the most common and challenging procedures, interventions and treatments in their practice. In addition, the clinical cadre propose provocative questions for added discussion to encourage creativity and lateral thinking.  Accompanying didactic lectures relate basic engineering principles to associated procedural medicine topics.

ESI – Methods⁺ (examples of satisfying courses are below, not a complete list): 

• Devices: CS 8395 — Internet of Medical Things

  • The course covers foundational topics for designing Internet of medical things (IoMT) solutions including systems (devices, interoperability, and integration), algorithms (data design, feature engineering, and time series machines learning), and commercialization (regulatory pathways and entrepreneurship). Case studies motivate challenges, solutions, and future opportunities in IoMT system and algorithm design to de-risk commercialization from concept to market adoption. Upon completion, students will be prepared to engage in commercially-viable IoMT research and development.

• Guidance and Delivery: EECE 8395 — Engineering for Surgery and Intervention

  • Students will gain expertise in a breadth of technical topics of interest in engineering in surgery and other medical interventions, with focus on both theory and project experience. Topics will include interactive data visualization and analysis, image acquisition and reconstruction, registration and optical tracking, image processing, machine learning and deep learning, and bio modeling.

• Image Analysis and Data Science: CS 5262 — Foundations of Machine Learning

  • Theoretical and algorithmic foundations of supervised learning, unsupervised learning, and reinforcement learning. Linear and nonlinear regression, kernel methods, support vector machines, neural networks and deep learning methods, instance-based methods, ensemble classifiers, clustering and dimensionality reduction, value and policy iteration. Explainable AI, ethics, and data privacy.

• Image Processing: CS 8395 — Open Source Programming for Medical Image Processing

  • This hands-on course introduces students to the open-source libraries, tools and techniques for solving medical image analysis problems in research, commercial and clinical settings. The topics will include open-source libraries for addressing visualization needs that arise in medical image analysis, as well as open-source cross-platform software as development based for advanced work. This course will also use best practices, such as version management, needed for generating reproducible results. 

• Imaging:  BME 7420 — Magnetic Resonance Imaging Methods

  • MR techniques to image tissue for clinical evaluation and research. RF pulses, k-space trajectories, chemical shift, motion, flow, and relaxation. Derivation of signal equations for pulse sequence design and analysis. Course includes hands-on experimental studies.
    

• Modeling:  BME 7310 — Advanced Computational Modeling and Analysis In Biomedical Engineering

  • By the end of this course, the student will understand the details of how to model different biological systems and some of the most current topics in modeling today.  Additionally, a sound understanding will be developed between the mathematics of models and their physiological counterparts for future work in biomedical simulation, imaging, and therapeutic/surgical guidance.  The student should gain a firm grasp of numerical methods for the solution of partial differential equations at the course conclusion.

• Robotics:  ME 5271 — Robotics

  • History and application of robots. Robotic mechanical architecture, mobility analysis of linkages, rotations and rigid body transformations and their parametrizations. Homogeneous coordinates of points and lines, exponential coordinates of rotation and twist coordinates, direct and inverse position analysis of serial manipulators and elimination theory. Serial robot statics and compliance, motion interpolation/path planning, instantaneous kinematics and Jacobian formulations. Lagrangian dynamics of serial robots, and motion control.

Professional Core⁺⁺:

• ENGM 6500 — Engineering Leadership and Program Management

• Students will learn to apply core principles of leadership and program management as engineering professionals. The course will cover strategic planning, people management, staffing, compensation, business process improvement theory, business interruption, leadership styles, emotional intelligence, negotiation and ethical business practices.

Design Core:

• Six credit hours of BME/ME/ or EECE 7899 are required

• Students in this course are immersed in an intensive design project working with both clinical and engineering mentors that is focused at cutting edge solutions to contemporary surgical and interventional problems using their enhanced skills in engineering design acquired over the course of their training program.

Electives:   

• The remaining 15 credit hours will be chosen from a number of electives. While not a complete list, some possible elective courses available are:

• BME 5400 Foundations of Medical Imaging

• BME 7110 Laser-Tissue Interaction and Therapeutic Use of Lasers

• BME 7310 Advanced Computational Modeling and Analysis in Biomedical Engineering

• BME 8901 Special Topics on Bioacoustics and Ultrasonic Imaging

• BME 8901 Advanced Drug Delivery

• EECE 6357 Advanced Image Processing

• EECE 6354 Intelligent Systems and Robotics

• CS 8395 Special Topics - Deep Learning in Medical Image Computing

• ME 5263 Computational Fluid Dynamics and Multiphysics Modeling

• ME 5271 Robotics

• ME 5284 Modeling and Simulation of Dynamic Systems

• ME 8331 Robotic Manipulators

• ME 8391 Special Topics in Robotics and Mechanism Synthesis