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3D Technologies for Medical Applications
Study Course Description
Course Description Statuss:Approved
Course Description Version:5.00
Study Course Accepted:09.10.2024 10:06:23
| Study Course Information | |||||||||
| Course Code: | FK_079 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Physics | Target Audience: | Rehabilitation; Medical Technologies; Medicine; Dentistry | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Jevgenijs Proskurins | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Department of Physics | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | Riga, 26a Anninmuizas boulevard, Floor No.1, Rooms 147 a and b, fizika rsu[pnkts]lv, +371 67061539 | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 0 | Class Length (academic hours) | 0 | Total Contact Hours of Classes | 0 | ||||
| Total Contact Hours | 0 | ||||||||
| Full-Time - Semester No.2 | |||||||||
| Lectures (count) | 1 | Lecture Length (academic hours) | 2 | Total Contact Hours of Lectures | 2 | ||||
| Classes (count) | 10 | Class Length (academic hours) | 3 | Total Contact Hours of Classes | 30 | ||||
| Total Contact Hours | 32 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Knowledge of informatics at the level of the High school curriculum. | ||||||||
| Objective: | To train students in spatial modeling, creation, acquisition, and improvement of spatial anatomical models, as well as preparation for 3D printing. To introduce students to various spatial modeling options and software, to allow students to create different complex digital spatial models and print them out. It is expected that the students who have completed the study course will be able to independently develop and prepare spatial models for 3D printing, using data from radiology examinations, and will be able to apply the acquired knowledge in their professional activities. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to 3D technology course: Importance of 3D technology in various industries, brief history and development. Introduction to 3D printing technology: advantages and applications, types of 3D printing processes and materials, challenges and limitations. Introduction to 3D Modeling: Basics of 3D modeling techniques and software tools, Exploration of various industries using 3D modeling, eg architecture, product design, and medicine. The economic impact, environmental sustainability, etc. | Lectures | 1.00 | computer room | |||||
| 2 | Introduction to Image Segmentation. Role of Image Segmentation in Radiology. Significance in diagnosis, treatment planning, and research. Basics of Medical Imaging. Overview of medical imaging techniques (CT, MRI, US). The 3D Image Segmentation Process. Pre-processing. Feature Extraction. Segmentation Techniques. Challenges. Applications of 3D Image Segmentation in Radiology. Ethical and Legal Considerations. Benefits of 3D Image Segmentation in Radiology. Future Directions. | Classes | 1.00 | computer room | |||||
| 3 | Medical imaging and 3D modeling, 3D printing for medical use, advanced applications of 3D modeling and 3D printing in medicine. Methods for converting medical images into 3D models. Accuracy and resolution considerations in medical 3D models. Examples of 3D modeling in medical research and clinical practice. Practical tasks. | Classes | 1.00 | computer room | |||||
| 4 | Application of (MM) to Medical Planning, Image/3D Model Based Diagnostics and Imaging, Medical Simulation. An overview of MM algorithms for classification and regression. Types of radiological examination images and their characteristics. Trait acquisition and selection methods in med. images. Applications of MM in radiol., including image segmentation and classificat. Application of the Python progr. lang. in the import, processing and visualization of radiological examination files. | Classes | 1.00 | computer room | |||||
| 5 | Automatic segmentation, principles and algorithms of segmentation, generation of spatial models from the result of segmentation, concept of artificial intelligence (MI) and its role in 3D technologies. Segmentation of radiological examinations using pre-trained neural networks. Working with Jupyter Notebook, connecting to a supercomputer, importing and processing data on a supercomputer. | Classes | 1.00 | computer room | |||||
| 6 | Basics of parametric and direct modeling of 3D models, 3D modeling from 2D sketches/drawings or surface scan images, recognition of spatial objects, basic functions in 3D modeling using the OnShape program, reverse engineering. | Classes | 1.00 | computer room | |||||
| 7 | Modeling of personalized medical devices, the use of implants and prostheses in medicine, including their historical development, types, materials used and comparison of traditional and personalized solutions. Modeling of implant prostheses. Hip implant. Patient anatomy for precise fit and function. Biomechanical factors for stability and strength. Selection of materials for biocompatibility and durability. Integration with existing anatomical structures. | Classes | 1.00 | computer room | |||||
| 8 | Modeling of surgical templates for precision surgery, use of surgical templates, their types and advantages. | Classes | 2.00 | computer room | |||||
| 9 | Work on the final project. Hands-on experience with 3D modeling software. Individual project demonstrating the use of 3D modeling and printing in medicine. | Classes | 2.00 | auditorium | |||||
| Assessment | |||||||||
| Unaided Work: | Practical tasks of radiological examination segmentation and 3D model processing. | ||||||||
| Assessment Criteria: | Active participation in practical lessons. Independent work 50%. Successful completion of a test in the form of a test in the e-study environment, which accounts for 50% of the final grade. | ||||||||
| Final Examination (Full-Time): | Exam | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | To provide students with insight and practical knowledge in 3D scanning and modeling, which students could potentially encounter in the future in their professional environment, thereby increasing their competitiveness. | ||||||||
| Skills: | As a result of the study course, students will be able to use the acquired knowledge of 3D scanning and modeling in order to be able to work practically with various 3D modeling programs, as well as to be able to apply these technologies in practice. It is expected that the students who have completed the study course will be able to independently develop and prepare spatial models for printing, using data from radiology examinations, will be able to apply the acquired knowledge in their professional activities. | ||||||||
| Competencies: | 1. Independently develops new - individually suitable for patients - unique digital models of implants and prostheses and prepares these models for production (uses and adapts technologies for manufacturing implants/prostheses). (3.1. Creation of digital content; 3.2. Integration and redevelopment of digital content; 4.2. Protection of personal data and privacy; 5.3. Creative use of digital technologies; DigComp 7) 2. Segments CT, CBCT and MRI examinations and creates personalized 3D models of anatomical structures , which can be used in planning the individual therapy of patients. (3.1. Creation of digital content; 3.2. Integration and redevelopment of digital content; 4.2. Protection of personal data and privacy; 5.3. Creative use of digital technologies; DigComp 7) 3. Uses and adapts scripts of various programming languages, e.g. Python for automated segmentation of anatomical structures adapted to each patient's individual medical history and available radiological examinations. (3.1. Creation of digital content; 3.2. Integration and redevelopment of digital content; 3.4. Programming; 4.2. Protection of personal data and privacy; 5.3. Creative use of digital technologies; DigComp 7) 4. Creates unique and specially adapted therapy solutions for patients (3D surgical planning, creation of implant models), develops these solutions in cases of limited data volume (limitations of radiological examinations) by combining various segmentation and 3D modeling software, e.g. Fusion 360, Blender, Meshmixer and 3-Matic Mimics Innovation Suite. (3.1. Creation of digital content; 3.2. Integration and redevelopment of digital content; 3.4. Programming; 4.2. Protection of personal data and privacy; 5.3. Creative use of digital technologies; 2.1. Interaction using digital technologies; 2.4. Collaboration using digital technologies ; DigComp 7). | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Introduction to Machine Learning with Python. by Andreas C. Müller, Sarah Guido. Released September 2016. Publisher(s): O'Reilly Media, Inc. | ||||||||
| 2 | 3D Deep Learning with Python. by Xudong Ma, Vishakh Hegde, Lilit Yolyan. Released October 2022. Publisher(s): Packt Publishing. | ||||||||
| Additional Reading | |||||||||
| 1 | Richard Szeliski. Computer Vision: Algorithms and Applications. 2nd ed. The University of Washington, Springer, 2022. | ||||||||
| 2 | Geoff Dougherty. Digital Image Processing for Medical Applications. California State University, Channel Islands, April 2009. | ||||||||
