CB7-That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader contexts related to their area of ??study CG2-Interact in a multicultural environment through knowledge of the national and European cultures, human rights and European realities CB8-That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments CG3-Communicate in an effective way both orally and in writing, preparing documents and presenting projects and results with English language CB10-That students have the learning skills to allow them to continue studying in a way that will mostly be self-directed or autonomous CT2-Interact in a multicultural environment through knowledge of the national and European cultures, human rights and European realities CG7-That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader contexts related to their area of ??study CT3-Communicate in an effective way both orally and in writing, preparing documents and presenting projects and results with English language CG8-That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments CG10-That students have the learning skills to allow them to continue studying in a way that will mostly be self-directed or autonomous CE2-Learn which algorithm(s) could be more suitable in a particular application CE9-Ability to implement and evaluate computer assisted detection algorithms, and critical ability to decide their daily clinical use CE38-Learn which algorithm(s) could be more suitable in a particular application CE59-Have a good knowledge of the field of computer-assisted detection (CADe) CE60-Analyze the state of the art of the segmentation algorithms used in the analysis of medical images, from the point of view of the computer vision engineer CE61-Ability to critically review, evaluate and apply a series of advanced techniques of medical image analysis and computer-aided diagnosis
To have a good knowledge of the field of Computer Aided Detection (CADe). To analyse the state of the art segmentation algorithms used in medical image analysis, from the perspective of the computer vision engineer. To be able to evaluate a segmentation algorithm and asses is usability for daily clinical usage. Estimate the crucial factors for it to be successful. To learn what algorithm(s) could fit better for a particular application.
1. Introduction to Medical Image Segmentation and Applications 2. Image preprocessing 3. Clustering segmentation techniques 4. Region-based segmentation techniques 5. Atlas based segmentation EM/Bayesian +atlas / Markov Random Fields 6. Segmentation via detection + Patches + classification 7. Deep learning for image segmentation (CNNs) 8. Evaluation of segmentation algorithms for medical applications
Activity type Hours with a teacher Hours without a teacher Virtual hours with a teacher Total Analysis / case study 8,00 25,00 0 33,00 Problem Based Learning (PBL) 10,00 50,00 0 60,00 Seminars 4,00 6,00 0 10,00 Theory class 22,00 25,00 0 47,00 Total 44,00 106,00 0 150
González, Rafael C. (2004). Digital image processing using Matlab. Upper Saddle River : Prentice Hall, cop. 2004. Forsyth, David A., Ponce, Jean (2003). Computer vision : a modern approach. Upper Saddle River: Prentice Hall. Robert B. Fisher (2007). CVonline: The Evolving, Distributed, Non-Proprietary. On-Line Compendium of Comp.
Assessment activities: Description of the activity Assessment Activity % Remediable subject Theoretical lectures There is an exam about the theoretical contents of the course 20 No Lab sessions Different lab assignments done during the course. Evaluation of the work done (code), obtained results and report. 50 Yes Segmentation project Final project assignment. Evaluation of the work done (code), obtained results and report. 30 Yes
The evaluation is based on three different activities: 50% from lab sessios P1 + 30% from the final project + 20% from an exam. Evaluation Criteria: From Labs/Project: 70% strategy and results + 30% document If fraudulent actions are detected in any type of academic activity (use of information without authorization, use of false information, use of unauthorized devices, impersonation, total or partial plagiarism, purchase and sale of tests, practices and assignments, etc) the students involved will automatically fail the subject. Depending on the type of fraudulent act, the School Management will initiate the appropriate procedures in accordance with Law 3/2022 of February 24 on University Coexistence (https://www.boe.es/eli/es/l/2022/02/24/3) Specific criteria for the "No show" grade: NP will be considered when the students do not submit any of the evaluation activities (Labs, Final project, or exam) Single Assessment: Students should do the final project and an exam related to the contents seen in the course Minimum requirements to pass: To pass the course students should have a minimum qualification of 5
To stablish the appointments students can use moodle or sent emails to the teachers. These appointments can be done online via google meet meetings or in presence (Teacher offices)
The communitation and interaction with the student will be mainly done via moodle, having also specific forums for all the activities. Students can also interact with the teacher via email or via videoconferences (google meet).